Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

Mario Manosalvas-Paredes; Ronald Roberts; Maria Barriera; Konstantinos Mantalovas

doi:10.3390/infrastructures5010004

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

Manosalvas-Paredes, Mario;Roberts, Ronald;Barriera, Maria;Mantalovas, Konstantinos 2019-12-30 00:00:00 infrastructures Article Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies 1 , 2 , 3 Mario Manosalvas-Paredes *, Ronald Roberts * , Maria Barriera and Konstantinos Mantalovas Nottingham Transportation Engineering Centre, University of Nottingham, Nottingham NG7 2RD, UK Dipartimento di Ingegneria, Scuola Politecnica, Ediﬁcio 8, Universitá degli Studi di Palermo, 90128 Palermo, Italy; konstantinos.mantalovas@unipa.it EIFFAGE Infrastructures GD—Research and Innovation Department, Corbas, CEDEX 09, 69960 Lyon, France; maria.barriera@eiage.com * Correspondence: ezzmam@nottingham.ac.uk (M.M.-P.); ronaldanthony.roberts@unipa.it (R.R.); Tel.: +44-7848-88-5919 (M.M.-P.); +39-348-442-8206 (R.R.) Received: 14 November 2019; Accepted: 26 December 2019; Published: 30 December 2019 Abstract: Road agencies are constantly being placed in dicult situations when making road maintenance and rehabilitation decisions as a result of diminishing road budgets and mounting environmental concerns for any chosen strategies. This has led practitioners to seek out new alternative and innovative ways of monitoring road conditions and planning maintenance routines. This paper considers the use of innovative piezo-ﬂoating gate (PFG) sensors and conventional strain gauges to continuously monitor the pavement condition and subsequently trigger maintenance activities. These technologies can help develop optimized maintenance strategies as opposed to traditional ad-hoc approaches, which often lead to poor decisions for road networks. To determine the environmental friendliness of these approaches, a case study was developed wherein a life cycle assessment (LCA) exercise was carried out. Observations from accelerated pavement testing over a period of three months were used to develop optimized maintenance plans. A base case is used as a guide for comparison to the optimized systems to establish the environmental impacts of changing the maintenance workﬂows with these approaches. On the basis of the results, the proposed methods have shown that they can, in fact, produce environmental beneﬁts when integrated within the pavement management maintenance system. Keywords: pavement management system; embedded sensors; piezoelectric sensors; accelerated pavement testing; life cycle assessment; environmental impact 1. Introduction 1.1. The Needs of Current Pavement Maintenance Systems and Practices In today’s global landscape, there are signiﬁcant challenges for development being faced by countries of all sizes and types. One of the primary concerns is the condition of the roadway network as this is a key determinant for development. This is because it is considered the gateway to mobility and access for citizens, which in turn leads to economic and social beneﬁts for the nation and its people [1]. This concern is further worsened by the continuing budget reductions for road authorities for pavement maintenance and rehabilitation programs [2]. These reductions result in authorities not having sucient ﬁnancial resources to maintain their networks in an optimal state. There are attempts to utilize optimization systems such as the pavement management system (PMS), which is based on utilizing the available ﬁnancial resources in the most ecient and valued Infrastructures 2020, 5, 4; doi:10.3390/infrastructures5010004 www.mdpi.com/journal/infrastructures Infrastructures 2020, 5, 4 2 of 20 manner based on the needs of the road network [3]. However, while the use of the PMS helps authorities to make optimized decisions, it is also highly dependent on the availability of data on the condition of the r Infrastructures oads in the 2020 network. , 5, x FOR PEER REVI The acquisition EW of this road condition data can be quite costly, as2 of the 20 most accurate technologies to date largely involve expensive equipment and vehicles featuring elements condition of the roads in the network. The acquisition of this road condition data can be quite costly, such as laser proﬁlers [4]. These systems in many cases require signiﬁcant time and substantial training as the most accurate technologies to date largely involve expensive equipment and vehicles featuring for the authorized personnel working in the road authorities. As a result of this, agencies quite often elements such as laser profilers [4]. These systems in many cases require significant time and rely on the use of manual surveys to obtain this data [5] and, as a result, these data can be considered substantial training for the authorized personnel working in the road authorities. As a result of this, subjective and, in some cases, inaccurate. This leads to the development of poor maintenance strategies agencies quite often rely on the use of manual surveys to obtain this data [5] and, as a result, these or, in many cases, a pre-set strategy with no capacity to adapt to real-time circumstances and challenges. data can be considered subjective and, in some cases, inaccurate. This leads to the development of To this end, it has been the goal of many authorities and agencies to ﬁnd lower-cost solutions for poor maintenance strategies or, in many cases, a pre-set strategy with no capacity to adapt to real- monitoring road conditions for the purpose of building accurate and robust asset databases. time circumstances and challenges. To this end, it has been the goal of many authorities and agencies There are several dierent areas of research in this domain [6,7]. Generally, the two most researched to find lower-cost solutions for monitoring road conditions for the purpose of building accurate and robust asset databases. areas of study of automated pavement distress collection systems are the ones based on systems There are several different areas of research in this domain [6,7]. Generally, the two most utilizing lasers and imaging technologies [8]. There are advantages and disadvantages of both of researched areas of study of automated pavement distress collection systems are the ones based on these systems, with the laser-based systems generally being more accurate, but the imaged-based ones systems utilizing lasers and imaging technologies [8]. There are advantages and disadvantages of carrying a lower cost. However, with both systems, there is still a need for continuous physical surveys both of these systems, with the laser-based systems generally being more accurate, but the imaged- to be carried out on the road to inspect conditions. based ones carrying a lower cost. However, with both systems, there is still a need for continuous Apart from the aforementioned technologies, there is signiﬁcant research built around the use physical surveys to be carried out on the road to inspect conditions. of in situ monitoring systems for the acquisition of accurate information concerning the conditions Apart from the aforementioned technologies, there is significant research built around the use of the pavement. Such systems allow documenting the level of service of the road asset via the use of in situ monitoring systems for the acquisition of accurate information concerning the conditions of of embedded sensors and technologies, which can allow for remote and continuous monitoring over the pavement. Such systems allow documenting the level of service of the road asset via the use of the life cycle of the pavement for fatigue [9–13]. Such systems do not require frequent surveys and embedded sensors and technologies, which can allow for remote and continuous monitoring over the state of the pavements can be monitored without any disruption to the trac or road network, the life cycle of the pavement for fatigue [9–13]. Such systems do not require frequent surveys and which the sta is an te of advantage the pavements over the can be moni systems mentioned tored without befor any di e. These sruption to the tra embedded fsystems fic or roatypically d networkwork , which is an advantage over the systems mentioned before. These embedded systems typically work by monitoring strains of the asphaltic layers, which can then be interpreted to help road agencies to by monitoring strains of the asphaltic layers, which can then be interpreted to help road agencies to discern information on the condition of the pavement. Accurate post-processing and analysis of data discern information on the condition of the pavement. Accurate post-processing and analysis of data coming from the sensors are fundamental in order to deﬁne an adapted and cost-eective management coming from the sensors are fundamental in order to define an adapted and cost-effective plan. The information can be commonly utilized within a PMS for particular needs, as shown in management plan. The information can be commonly utilized within a PMS for particular needs, as Figure 1 below [14]. The full extent to which the information can be utilized within the PMS is not shown in Figure 1 below [14]. The full extent to which the information can be utilized within the PMS covered within this study, but the ways in which the data collected can be used for determining needs is not covered within this study, but the ways in which the data collected can be used for determining of the pavement and for planning interventions are the main focus of the work. Furthermore, the data needs of the pavement and for planning interventions are the main focus of the work. Furthermore, obtained through the sensors can be considered under information quality level 4, which considers the the data obtained through the sensors can be considered under information quality level 4, which structure and condition of the pavement for planning and performance evaluation. considers the structure and condition of the pavement for planning and performance evaluation. Determining needs of road Addressing impacts of different strategies (What If scenarios) Providing project and treatment recomendations Allocation of funds Setting performance targets Long-term planning Communication of current network conditions Figure Figure 1. 1. Typi c Typical uses of pavement manage al uses of pavement managemen ment info t informa rmation in a pavement managemen tion in a pavement management s ty system stem (PMS). (PMS). Infrastructures 2020, 5, 4 3 of 20 The use of the data can, therefore, extend the road service life and improve its safety. For the purpose of this paper, conventional strain gauges and piezoelectric sensors are considered for monitoring road conditions, and consequently triggering maintenance activities. An insight into their use is explored and the possibilities of their use in road condition monitoring are identiﬁed through an experimental case study. 1.2. Environmental Concerns about Employing New Detection Systems With the use of these embedded technologies, it is possible for early detection of pavement distresses and for preventative maintenance to be employed instead of the costlier corrective maintenance practices that would be needed once the pavement would have already failed [15–18]. In scenarios typical in small authorities, there is usually a pre-set maintenance plan for the life cycle of the pavement based on the experience of the area and the available funds. With the use of these types of maintenance plans, there is no customization based on the real conditions of the roads. Therefore, limited preventative maintenance is done to help lengthen the pavement life cycle and save money for the authority. They essentially operate on the ‘worst-ﬁrst’ approach, wherein the pavements are allowed to reach their failure point without any preventative measures deployed [1]. As a result, the use of embedded sensors would be a welcome addition for the authority. However, the use of these proactive sensors can result in more frequent maintenance interventions as more preventative interventions would be utilized based on the triggers of the sensors to delay the use of the more costly corrective maintenance interventions when the pavement has suered both functional and structural failure. This result brings into question the environmental friendliness of using these approaches, as more frequent interventions can have more severe environmental impacts. Transportation can be quite energy-intensive, and thus the associated environmental impacts can be adverse. In many cases, however, the construction, operation, and maintenance of the road pavements or road networks have been considered less signiﬁcant in terms of environmental impacts, when compared with the potential environmental impacts by the vehicles utilizing the speciﬁc road or road network during its life cycle [19,20]. Given the need for more sustainable transportation infrastructures and asphalt pavements, it has lately become apparent that aspects such as the road construction and maintenance could lead to signiﬁcantly increased amounts of energy consumed, and hence to higher amounts of emissions [19,21]. To further investigate, the aforementioned environmental implications the life cycle assessment methodology can be utilized as described in international standards [22,23]. This study, however, has as a main objective to compare the environmental impacts of three dierent alternatives, namely, three dierent maintenance pipelines, focusing only on the use phase of the asphalt road and speciﬁcally on its maintenance. Numerous studies have been conducted so far that assess the environmental impacts of asphalt pavements over their life cycle. For instance, Häkkinen & Mäkele assessed the environmental impacts of the pavement construction, maintenance, and trac, followed by Chappat and Bilal, who also focused on the same aspects of the environmental assessment of a road [24,25]. Other researchers also included the environmental impacts arising owing to the construction of the necessary earthworks, surrounding a road pavement [19,26], while Hoang et al. only assessed the environmental impacts of the road construction and maintenance [27]. It thus becomes evident that the use of life cycle assessment for roads is strongly correlated with the objective of the study and can be implemented. In the speciﬁc investigation, as it has a comparative nature, the comparison is undertaken with the same pavement structure. However, with alternative maintenance strategies each time, the stage of pavement construction, the impacts of the earthworks’ construction, the trac impacts, and the end of life were omitted from the study. This is because of the fact that a comparative study would not beneﬁt from the inclusion of identical aspects in all the alternatives. In other words, the omitted aspects would have no inﬂuence on the outcomes of the study. Infrastructures 2020, 5, 4 4 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 4 of 20 1.3. Aim of the Study 1.3. Aim of the Study This paper carried out a life cycle assessment (LCA) case study to quantify the environmental This paper carried out a life cycle assessment (LCA) case study to quantify the environmental impacts of the maintenance pipelines based on three dierent scenarios (technologies). The baseline impacts of the maintenance pipelines based on three different scenarios (technologies). The baseline scenario is where no sensors are embedded in the pavement structure, and thus a preset maintenance scenario is where no sensors are embedded in the pavement structure, and thus a preset maintenance plan is followed; the second scenario utilizes piezo-electric sensors and the third utilizes conventional plan is followed; the second scenario utilizes piezo-electric sensors and the third utilizes conventional strain gauges, in order for optimized maintenance pipelines to be achieved. The LCA was carried out strain gauges, in order for optimized maintenance pipelines to be achieved. The LCA was carried out using results from the experimental test section, where the sensors were deployed in an accelerated using results from the experimental test section, where the sensors were deployed in an accelerated pavement testing setup. Environmental impacts of devising maintenance plans based on intervention pavement testing setup. Environmental impacts of devising maintenance plans based on intervention triggers from the sensors as opposed to a typical pre-set plan were compared and analyzed. triggers from the sensors as opposed to a typical pre-set plan were compared and analyzed. 1.4. Structure of the Study 1.4. Structure of the Study Before the LCA case study could be done, it was also very important to understand how the Before the LCA case study could be done, it was also very important to understand how the sensor results are read and interpreted, as this will establish the practicality of using them in real-world sensor results are read and interpreted, as this will establish the practicality of using them in real- conditions. To this end, Section 2 describes the gauges and sensors utilized in the study and Section 3 world conditions. To this end, Section 2 describes the gauges and sensors utilized in the study and explains the experimental setup of the study. The results of the sensors in the case study are then Section 3 explains the experimental setup of the study. The results of the sensors in the case study are provided in Section 4, detailing how results are read and analyzed, whereas Section 5 deals with the then provided in Section 4, detailing how results are read and analyzed, whereas Section 5 deals with formulation of the maintenance strategies. Finally, the results of the LCA are provided in Section 6, the formulation of the maintenance strategies. Finally, the results of the LCA are provided in Section with further discussions being made on the results of the LCA and the use of the embedded sensors. 6, with further discussions being made on the results of the LCA and the use of the embedded sensors. 2. Embedded Sensors Considered in the Study 2. Embedded Sensors Considered in the Study 2.1. Strain Gauges 2.1. Strain Gauges This study used a strain gauge denoted as type KM-100HAS, provided by TML (Tokyo Measuring Instruments This study Laboratory used aCo., stra Ltd.—T in gau okyo ge denot Sokkied Kenkyujo) as type (Figur KM-10 e0 2H ).A This S, provided device is b waterpr y TML oof, (To and kyo it Measur is designed ing In tostruments withstandLaboratory Co high temperatur ., Ltd.—To es and compaction kyo Sokki Kenk loads,yujo) usually (Figure associated 2). This d withe asphalt vice is pavement waterproof constr , and uction it is [des 28].igned t Strainsoar w e isensed thstand h when igh the teﬂanges mperatures are slightly and com varied pacby tion lo strains ads, generated usually a inside ssociathe ted with a asphaltsphal and small t pavement constructi displacements ar on [28 e transferr ]. Strain ed s are se to the spring nsed when the flange element. Asphalt s are strains slightly are va then ried by strai convertedninto s genera electrical ted insignals side the and aspha read lt a out nd by sma all d data isp acquisition lacements are tr system. ans T fypical erred to the solutions spring for element. the transmission Asphalt of strains the data are then conver use externalted into e boxes forlec storing trical s the igncables als and re (wirad o es) and ut by a the d data ata ac acquisition quisition system. Typ system. These ical boxes solutions can be for the trans located atm the issr ion of th oadsidee data and can use ex be power ternal boxes ed by solar for storing the panels, allowing cables (wire data transmission s) and the dto ata a acq cloud uisit server ion syst . The em transducer . These bo has xes an caappar n be lo ent cat elastic ed at modulus the roads ofide appr and oximately can be powered by 40 N/mm , r esistance solar panels, of 350 allow ohming (W)data tran full bridge, smis rated sion to a clo output appr ud serve oximately r. Thof e trans 2.5 mV duce /V,rcapacity has an a of ppa 5000 rent ela 10 stic strain, modulus of a and a temperatur pproximately 4 e range 0 N/m between m , resi20 stan Cce of and 35 180 0 ohm C. Equation (Ω) full brid (1) shows ge, rat the ed −6 output calculation approxi method mately of 2.5 when variation mV/V, ca in temperatur pacity of ±50 e is 00 ignor × 10ed, strain, whereand " corr a temper esponds ature to r the ange strain between value 6 6 6 (− 20 10 °C ),aC nd to 18 the 0 °C. Equa calibration tion (1 coe ) cient shows the cal (10 /1c 10 ulat), ion method and " to the when va measurri ed atchange ion in tempera from theture i initial s " i −6 −6 −6 value ignore(d,10 wher ) considering e ε1 corresponds to the stra a gauge factoriof n v two. alue (×10 ), Cε to the calibration coefficient (10 /1 × 10 ), −6 and εi to the measured change from the initial value (×10 ) considering a gauge factor of two. " = C " (1) 1 " i 𝜀 =𝐶 ∗𝜀 (1) Figure 2. Figure 2. Tokyo Meas Tokyo Measuring uring I Instr nstruments uments L Lab ab (TML (TML) ) strain strain g gauge. auge. Infrastructures 2020, 5, 4 5 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 5 of 20 2.2. Piezoelectric Sensors 2.2. Piezoelectric Sensors Piezoelectric sensors have become more popular in strain and vibration sensing owing to their Piezoelectric sensors have become more popular in strain and vibration sensing owing to their ability to harvest mechanical energy from ambient variations [10,29]. Recent research has shown that ability to harvest mechanical energy from ambient variations [10,29]. Recent research has shown that piezoelectric transducers can be used to self-power sensors for long-term monitoring applications [13]. piezoelectric transducers can be used to self-power sensors for long-term monitoring applications Under trac loading, piezoelectric sensors harvest the induced micro-strain energy in the asphalt [13]. Under traffic loading, piezoelectric sensors harvest the induced micro-strain energy in the concrete (AC) layer to power the sensor electronics and to assess the pavement condition. In this asphalt concrete (AC) layer to power the sensor electronics and to assess the pavement condition. In study, a rectangular polyvinylidene ﬂuoride (PVDF) ﬁlm is used to convert strain energy into an this study, a rectangular polyvinylidene fluoride (PVDF) film is used to convert strain energy into an electrical signal. The open source voltage (V) generated by a PVDF ceramic transducer material can be electrical signal. The open source voltage (V) generated by a PVDF ceramic transducer material can calculated using Equation (2), where S, Y, d , h, and ", are the applied strain, Young’s modulus of be calculated using Equation (2), where S, Y, d31, h, and ε, are the applied strain, Young’s modulus of the piezoelectric material, piezoelectric constant, thickness, and electrical permittivity, respectively. the piezoelectric material, piezoelectric constant, thickness, and electrical permittivity, respectively. The generated energy (E ) from a piezoelectric transducer across a load resistance (R) is shown in The generated energy (En) from a piezoelectric transducer across a load resistance (R) is shown in Equation (3), where t is the loading time. Equation (3), where f tf is the loading time. 𝑆 𝑌 𝑑 ℎ S Y d h (2) 𝑉= V = (2) 𝑉 𝑡 𝐸 = 𝑑𝑡 (3) ( ) V t E = dt (3) For the purpose of this study, a particular type of piezoelectric sensor was utilized—the recently For the purpose of this study, a particular type of piezoelectric sensor was utilized—the recently developed piezo-floating-gate (PFG) sensor, Figure 3. This sensor is equipped with a series of developed piezo-ﬂoating-gate (PFG) sensor, Figure 3. This sensor is equipped with a series of memory memory cells that successively store the duration of strain events. The PFG sensor starts measuring cells that successively store the duration of strain events. The PFG sensor starts measuring when when the amplitude of the input signal, coming from the piezoelectric transducer, exceeds one or the amplitude of the input signal, coming from the piezoelectric transducer, exceeds one or more more threshold [30,31]. The piezoelectric sensor can incorporate an antenna for direct (wireless) data threshold [30,31]. The piezoelectric sensor can incorporate an antenna for direct (wireless) data transmission. A reader (in a form of a USB) then communicates with the sensor through a specific transmission. A reader (in a form of a USB) then communicates with the sensor through a speciﬁc radio radio frequency; initial experiments have shown that data transmission could be done up to a frequency; initial experiments have shown that data transmission could be done up to a maximum car maximum car speed of 70 km/h. speed of 70 km/h. Figure 3. Piezo ﬂoating gate (PFG) sensor composition. PVDF, polyvinylidene ﬂuoride. Figure 3. Piezo floating gate (PFG) sensor composition. PVDF, polyvinylidene fluoride. Sensor results can be characterized by the following cumulative distribution (CDF) function, Sensor results can be characterized by the following cumulative distribution (CDF) function, Equation (4), where is the mean of the deformation distribution, is the standard deviation Equation (4), where μ is the mean of the deformation distribution, σ is the standard deviation considering load and frequency variability, and is the total cumulative time of the applied strain. considering load and frequency variability, and α is the total cumulative time of the applied strain. The statistical parameters and of the deformation distribution can be considered as indicators of The statistical parameters μ and σ of the deformation distribution can be considered as indicators of damage progression. In fact, and are the only viable tools to analyze the results delivered by the damage progression. In fact, μ and σ are the only viable tools to analyze the results delivered by the PFG sensor. These parameters are obtained by means of a curve adjustment of the sensor distribution PFG sensor. These parameters are obtained by means of a curve adjustment of the sensor distribution results taken from the memory cells (D1–D7) [31]. results taken from the memory cells (D1–D7) [31]. " !# (" ) 𝛼 𝜀 𝜇 F(") = 1 er f (4) 𝐹 𝜀 = 1 𝑒 (4) 𝜎 2 𝑟𝑓 Infrastructures 2020, 5, 4 6 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 20 3. Experimental Test Section 3. Experimental Test Section 3. Experimental Test Section To analy To analy ze th ze th e use e use of the of the aforementione aforementione d techno d techno log log iei se , s an exp , an exp ere im rim ent ent al al set set up u w p w as orch as orch est est rat rat ed e d To analyze the use of the aforementioned technologies, an experimental setup was orchestrated wherein the technologies were set up in a situation simulating real-world conditions. The experiment wherein the technologies were set up in a situation simulating real-world conditions. The experiment wherein the technologies were set up in a situation simulating real-world conditions. The experiment was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which is an accelerated pavement testing (APT) facility owned and managed by The French Institute of is an accelerated pavement testing (APT) facility owned and managed by The French Institute of is an accelerated pavement testing (APT) facility owned and managed by The French Institute of Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies were powered by an external data center owned and operated by IFSTTAR. This setup also allowed were powered by an external data center owned and operated by IFSTTAR. This setup also allowed were powered by an external data center owned and operated by IFSTTAR. This setup also allowed for for the transmission of data during the experiment. Within Figure 4, the precise location of the sensors for the transmission of data during the experiment. Within Figure 4, the precise location of the sensors the transmission of data during the experiment. Within Figure 4, the precise location of the sensors is is shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four is shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to half arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to half arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to of the standard French equivalent axle load [32]. The loading program began on 14 November 2017 of the standard French equivalent axle load [32]. The loading program began on 14 November 2017 half of the standard French equivalent axle load [32]. The loading program began on 14 November and finished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an and finished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an 2017 and ﬁnished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). Figure 4. Distribution of sensors within the pavement structure. Figure Figure 4. 4. Distribution Distribution of s of sensors ensors within the within the pavement pavement stru structur ctue. re. The tested pavement structure is ﬂexible by nature and comprised of 10 cm of bituminous surface The tested pavement structure is flexible by nature and comprised of 10 cm of bituminous The tested pavement structure is flexible by nature and comprised of 10 cm of bituminous and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The structure of surface and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The surface and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The the pavement is depicted in Figure 5. The pavement conﬁguration was done given typical testing structure of the pavement is depicted in Figure 5. The pavement configuration was done given typical structure of the pavement is depicted in Figure 5. The pavement configuration was done given typical conditions for testing new materials and equipment by IFFSTAR. testing conditions for testing new materials and equipment by IFFSTAR. testing conditions for testing new materials and equipment by IFFSTAR. Figure 5. Structure of pavement used in the test section. Figure 5. Structure of pavement used in the test section. Figure 5. Structure of pavement used in the test section. The section was instrumented with two horizontal strain gages placed at the bottom of the The section was instrumented with two horizontal strain gages placed at the bottom of the asphalt The section was instrumented with two horizontal strain gages placed at the bottom of the layer asphin altthe layer longitudinal in the long diirtection, udinal d asirec well tion, as w as six PFG ell as s sensors ix (ﬁve PFG sensors in the longitudinal (five in thdir e long ection itudin andal asphalt layer in the longitudinal direction, as well as six PFG sensors (five in the longitudinal direction and one in the transverse direction), as shown in Figure 4. The above-mentioned sensors one in the transverse direction), as shown in Figure 4. The above-mentioned sensors were placed at direction and one in the transverse direction), as shown in Figure 4. The above-mentioned sensors the were placed center of the at the center APT wheelof the APT path. The w radius heel p of ath. Th the cire rad cularius o wheel f the circ path exhibits ular wh aeel p radius ath exhibits a of 19.0 m. were placed at the center of the APT wheel path. The radius of the circular wheel path exhibits a radius of 19.0 m. Temperature variations in the asphalt layer were recorded at the top, middle, and Temperature variations in the asphalt layer were recorded at the top, middle, and bottom of the AC. radius of 19.0 m. Temperature variations in the asphalt layer were recorded at the top, middle, and bottom of the AC. bottom of the AC. Infrastructures 2020, 5, 4 7 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 7 of 20 Temperature measurements were performed at time intervals of 10 min over the duration of the Temperature measurements were performed at time intervals of 10 min over the duration of the test. Strain gages and PFG sensors measurements were recorded at approximately every 20,000 load test. Strain gages and PFG sensors measurements were recorded at approximately every 20,000 load repetitions. Figure 6 shows the responses from sensors T1 and H3 after 5000 load repetitions (1250 cycles). repetitions. Figure 6 shows the responses from sensors T1 and H3 after 5000 load repetitions (1250 This loading was based on sensor survivability tests carried out to ensure all the sensors utilized in the cycles). This loading was based on sensor survivability tests carried out to ensure all the sensors study were still alive. As can be seen, the sensors clearly respond to the passing arms of the carousel. utilized in the study were still alive. As can be seen, the sensors clearly respond to the passing arms The maximum strain (from TML sensors) and voltage (from PFG sensors) were considered for the of the carousel. The maximum strain (from TML sensors) and voltage (from PFG sensors) were purposes of this paper. For the purpose of applying the sensors in real-world conditions, the trac considered for the purposes of this paper. For the purpose of applying the sensors in real-world wander, and particularly the wheel wander, are important to ensure sensors are being deployed conditions, the traffic wander, and particularly the wheel wander, are important to ensure sensors along the correct paths [30]. Wandering distribution is studied through eleven positions, equidistant are being deployed along the correct paths [30]. Wandering distribution is studied through eleven every 0.11 m. Position one is at 18.48 m from the center of the carousel, while position eleven is at positions, equidistant every 0.11 m. Position one is at 18.48 m from the center of the carousel, while 19.53 m. The loading program followed a Gaussian distribution, where position six located at radius position eleven is at 19.53 m. The loading program followed a Gaussian distribution, where position 19.0 m supports 22.0% of the total loads and positions from one to ﬁve supported 1%, 3%, 7%, 11%, six located at radius 19.0 m supports 22.0% of the total loads and positions from one to five supported and 17% respectively. 1%, 3%, 7%, 11%, and 17% respectively. Figure Figure 6. 6. Measur Measurement from sensors T1 ement from sensors T1 (TM (TML) L) ( (top top)) and and sensors sensors H3 H3 (PF (PFG) G) ( (bottom bottom), ), after after 5000 5000 loads. loads. 4. Sensor Results 4. Sensor Results To allow for the application of the sensors in practical ﬁelds, it is important to understand how To allow for the application of the sensors in practical fields, it is important to understand how they generally function and the types of results that are generated from the sensors for dierent load they generally function and the types of results that are generated from the sensors for different load responses. To this end, Figure 7 shows the average longitudinal strain response from both TML responses. To this end, Figure 7 shows the average longitudinal strain response from both TML sensors during the APT experiment, where a clear dierence in terms of the maximum value is seen. sensors during the APT experiment, where a clear difference in terms of the maximum value is seen. This highlights how the maximum values utilized for this study were ascertained. Figure 7 (left) shows the responses from T1 where a constantly increasing trend occurs until 591,200 load Infrastructures 2020, 5, 4 8 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 20 This highlights how the maximum values utilized for this study were ascertained. Figure 7 (left) shows Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 20 the responses from T1 where a constantly increasing trend occurs until 591,200 load applications where applications where its maximum value, 123.3 microstrain (µs), is reached. On the other hand, Figure its maximum value, 123.3 microstrain (s), is reached. On the other hand, Figure 7 (right) shows 7 (right) shows the responses from T2 where its maximum value, 1446.7 µs, is reached at 820,000 load applications where its maximum value, 123.3 microstrain (µs), is reached. On the other hand, Figure the responses from T2 where its maximum value, 1446.7 s, is reached at 820,000 load repetitions. repetitions. It 7 (right) show is worth mentioning how s the responses from T2 w the co here its mpression–tension maximum value, 14 –c 46 ompressi .7 µs, is rea on c(CTC) cycle c hed at 820,000 l ho ange ad s It is worth mentioning how the compression–tension–compression (CTC) cycle changes between the between the two TML repetitions. It is worth mentioning how gauges. Table 1 summar the coimpression–tension zes the CTC cycle –c and F ompressi igureon 7 d (CTC) cycle c epicts how the sh hangeape s two TML gauges. Table 1 summarizes the CTC cycle and Figure 7 depicts how the shape of the between the two TML gauges. Table 1 summarizes the CTC cycle and Figure 7 depicts how the shape of the longitudinal strain changes with the increasing number of loads. It is known that the shape longitudinal of the long strain itudina changes l strain c with hange the s wit incrh easing the incre number asing number o of loads. Itf l is oads known . It is that known t the shape hat th will e shape not be will not be entirely symmetrical because of the viscoelastic behavior of the asphalt concrete (AC); entir wi ely ll not be enti symmetrical rely because symmetri of cal the because of viscoelastic the vi behavior scoela of sti the c beha asphalt vior of concr the a etes(AC); phalt concrete nonetheless, (AC); it is nonetheless, it is observed for both TML sensors how the CTC decrease/decrease/increase with nonetheless, it is observed for both TML sensors how the CTC decrease/decrease/increase with observed for both TML sensors how the CTC decrease/decrease/increase with damage. damage. damage. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Table 1. Maximum and minimum values for longitudinal strain gauges with load applications. TML, Tokyo Measuring Instruments Lab. Table 1. Table 1. Maximum a Maximum a nd n minimum values d minimum values for longitud for longitudinal st inal strain gau rain gau g g es wi es wi th load applic th load applic ata ions. TM tions. TM L, L, Tokyo Measuring Instruments Lab. Tokyo Measuring Instruments Lab. TML–T1 TML–T2 Load Applications TML–T1 TML–T2 TML–T1 TML–T2 Min-1 Max Min-2 Min-1 Max Min-2 Load Applications Load Applications Min-1 Max Min-2 Min-1 Max Min-2 Min-1 Max Min-2 Min-1 Max Min-2 5000 22.7 87.7 16.2 21.6 105.5 13.3 5000 −22.7 87.7 −16.2 −21.6 105.5 −13.3 5000 −22.7 87.7 −16.2 −21.6 105.5 −13.3 591,200 18.6 123.3 28.2 64.8 641.6 49.6 591,200 −18.6 123.3 −28.2 −64.8 641.6 −49.6 820,000 N/A N/A N/A 161.9 1446.7 190.2 591,200 −18.6 123.3 −28.2 −64.8 641.6 −49.6 820,000 N/A N/A N/A −161.9 1446.7 −190.2 999,200 13.5 13.2 55.8 72.1 309.4 364.8 820,000 N/A N/A N/A −161.9 1446.7 −190.2 999,200 −13.5 13.2 −55.8 −72.1 309.4 −364.8 999,200 −13.5 13.2 −55.8 −72.1 309.4 −364.8 It was also noted that there is an eect caused by the movement of the load on the surface. This is It was also noted that there is an effect caused by the movement of the load on the surface. This It was also noted that there is an effect caused by the movement of the load on the surface. This important as this must be considered when identifying the location for the installation of the sensors. is important as this must be considered when identifying the location for the installation of the is important as this must be considered when identifying the location for the installation of the Figure 8 shows the eect of distributing the load on the surface, wandering, where it is seen how the sensors. Figure 8 shows the effect of distributing the load on the surface, wandering, where it is seen sensors. Figure 8 shows the effect of distributing the load on the surface, wandering, where it is seen responses decrease its values as the load moves further from the center, radius 19.0 m. Figure 8 (left), how the responses decrease its values as the load moves further from the center, radius 19.0 m. Figure how the responses decrease its values as the load moves further from the center, radius 19.0 m. Figure PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 (right), 8 (left), PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 8 (left), PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 PFG–H5, shows how the signal disappears with respect to the load positioning. (right), PFG–H5, shows how the signal disappears with respect to the load positioning. (right), PFG–H5, shows how the signal disappears with respect to the load positioning. Figure Figure 8. 8. Evolution Evolution of of voltage voltage for for PFG PFG–H3 –H3 and PFG and PFG–H5, –H5, res respectively pectively. . Figure 8. Evolution of voltage for PFG–H3 and PFG–H5, respectively. Infrastructures 2020, 5, 4 9 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 20 Finally, the most important result from the sensors was the comparison of the evolution of the Finally, the most important result from the sensors was the comparison of the evolution of the Finally, the most important result from the sensors was the comparison of the evolution of the maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. The figures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG The ﬁgures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG The figures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on the same figure to allow for a combined visualization of the trends over the loading period. Figure 9 the same ﬁgure to allow for a combined visualization of the trends over the loading period. Figure 9 the same figure to allow for a combined visualization of the trends over the loading period. Figure 9 shows an inverse trend around 600,000 load repetitions (12 years), where the responses suffer an shows an inverse trend around 600,000 load repetitions (12 years), where the responses suer an shows an inverse trend around 600,000 load repetitions (12 years), where the responses suffer an important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when the responses rapidly increase. the responses rapidly increase. the responses rapidly increase. Figure 9. Sensors T1 and H3 measurements evolution. Figure 9. Sensors T1 and H3 measurements evolution. Figure 9. Sensors T1 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Infrastructures 2020, 5, 4 10 of 20 5. Development of Maintenance Intervention Strategies Given the results from the sensors and gauges, the next step in the study was the development of maintenance intervention strategies based on triggers that could be inferred from the results. To allow for this comparison to be done, a baseline was created of a typical predeﬁned maintenance strategy, over a 30-year period. Environmental impacts imposed during the maintenance of a speciﬁc road stretch could then be compared with the equivalent impacts originating from the maintenance strategy of the same road stretch and period, after utilizing information from the sensors and gauges, optimizing the maintenance strategy. The current approach was setup based on inputs from local experts and previous work in the sector on baseline maintenance strategies [33,34]. This baseline pre-set maintenance plan is given below in Table 2. Table 2. Pre-set maintenance actions for road under analysis. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Baseline 0 0 0 0 0 0 0 0 1,2 0 0 0 0 0 0 4 0 0 0 0 0 0 0 1,2 0 0 0 0 0 0 5 Interventions Where: 0: Do nothing 1: Cracks, rutting, potholes ﬁlling and sealing [Routine Maintenance] 2: Microsurfacing [Routine Maintenance] 3: Thin Overlay—2 cm Hot Mix Asphalt (HMA) [Preventative Maintenance] 4: Conventional structural mill and replace, Wearing course only [Corrective Maintenance] 5: Conventional structural mill and replace, wearing and binder [Corrective Maintenance] The optimized approaches were then generated utilizing information from the gauges as triggers for intervention strategies on the pavement, and are explained in the following section. 5.1. Optimized Plan Based on PFG Sensors Response In order to determine an optimized plan for the PFG sensors, a correct threshold deﬁnition is essential for the cumulative voltage time (CVT) approach. This study used percentiles P-95 and P-05 to deﬁne the upper and lower limits (D7 and D1, respectively) based on the entire responses for each PFG sensor. Once D1 and D7 have been deﬁned, equally space values are calculated (D2 to D6). Table 3 shows the seven thresholds for PFG–H3/H7 and activation number for the “waking-up” of the sensor along with the number of load applications. “Waking-up” of the sensor signiﬁes that it starts recording after a particular number of load applications. Figure 11 (top) shows the CVT for sensor H3, whereas Figure 11 (bottom) shows the CVT for sensor H7. Table 3. Threshold deﬁnition and activations. H3 H7 Threshold Deﬁnition Activation Deﬁnition Activation 1 0.018 – 0.016 – 2 0.025 – 0.028 – 3 0.031 591,200 0.038 125,600 4 0.038 591,200 0.048 125,600 5 0.045 789,200 0.059 N/A 6 0.051 789,200 0.069 N/A 7 0.058 789,200 0.080 N/A Infrastructures 2020, 5, 4 11 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 11 of 20 Figure 11. Cumulative voltage time (CVT) for sensor H3 and H7, respectively. Figure 11. Cumulative voltage time (CVT) for sensor H3 and H7, respectively. Using Figures 9–11, there are speciﬁc points that clearly stand out where the change in response, slope, denotes Using Figur the e appearance s 9–11, there are specific po of damage. Figur ints that e 11 (top) clearly stand o supports the ut where the chan previous statement ge in re wher sponse, e slope, denotes the appearance of damage. Figure 11 (top) supports the previous statement where the the “waking-up” of threshold level 3 and onwards occurs. Points located at 125,000, 591,200, and “waking-up 789,200 load ” of threshold repetitions (3.0, level 12.0, 3 anand d on16.0 wards occ years,u rrs espectively) . Points loca wer ted a e thus t 125selected ,000, 591,200 for developing , and 789,200 load repetitions (3.0, 12.0, and 16.0 years, respectively) were thus selected for developing the the maintenance strategies. ma At intena a loading nce stra value tegies. of 125,000 (equivalent to 3 years of the life cycle), threshold level 2 comes alive. At a loading value of 125,000 (equivalent to 3 years of the life cycle), threshold level 2 comes This can be considered as a point where the post-compaction of the AC layer is concluded and damage starts alive. This c to grow, but an be at consider this poined t, itas is also a point consider where th ed as e po low st-compactio severity distr n of the ess. Given AC layer that the is conc sensors luded have and damage starts to grow, but at this point, it is also considered as low severity distress. Given that the indicated that there is something wrong, the routine maintenance that was expected to be carried out at sensors have year 8 can be moved indicated th up toat there is some now take placething wrong two years after , the routine m the sensors have ainten indicated ance that was expect that something ed to be carried out at year 8 can be moved up to now take place two years after the sensors have indicated is wrong. This intervention is routine maintenance, characterized by minor surface treatments. that The somet nexthinstance ing is wron of noted g. This int change erven bytthe ion is ro sensors utin is e maint at an equivalent enance, char load actof eri591,200 zed by minor (equivalent surface treatments. to 12 years of the life cycle). At this point, the values of threshold levels 1 and 2 have kept growing The next instance of noted change by the sensors is at an equivalent load of 591,200 (equivalent to 12 years of the life cycle). At this point, the values of threshold levels 1 and 2 have kept growing as Infrastructures 2020, 5, 4 12 of 20 as expected. The damage is now considered as medium severity, as threshold levels 1 and 2 have changed their slope and threshold levels 3 and 4 are becoming alive as well. Therefore, given this development, another intervention at year 14 can be planned. At year 13 (two years after the trend has now picked up on the sensor/gauge), intervention 1 and 3 can be introduced instead of waiting for year 15, as was planned in the pre-set maintenance plan. Additionally, intervention 2 can be introduced midway between these planned interventions given the fact that the sensor shows the distress is evolving, and this would enable its development to be reduced, prolonging the necessity of corrective maintenance. At a loading of 789,200 (equivalent to year 16 in the life cycle), the values at threshold levels 1 and 2 grow with no variation in their slope; nonetheless, the values of threshold levels 3 and 4 have increased and threshold levels 5, 6, and 7 have been initiated. This, therefore, infers that the damage is now considered as medium/high severity, indicating that surface cracks are imminent. Therefore, in the optimized plan, interventions can be planned to extend the point at which this will happen. Subsequently, using intervention 3 along with the routine maintenance would be an intervention of less ﬁnancial impact than that of intervention 4, which is scheduled at year 15 in the baseline. It is necessary to note here that, while the sensors in the experimental section only provided data for 20 years, these data do allow for a projection of what can happen over a 30-year period, and thus enable the environmental evaluation over this period. This is based on the common interventions over similar times [33], and thus enable the environmental evaluation over this period. As interventions in years 0–20 have been shifted, the subsequent interventions in the following 10 years can then be shifted as well. Therefore, the next intervention to account for is intervention 4 (shown at year 15 in the baseline). In the optimized approach, intervention 1 can be introduced at year 19 (given the inclusion of a thin overlay at year 13), which accounts for routine surface maintenance. This is in line with the visual inspections in the study, showing cracking appearing at a loading of 910,000 (equivalent to year 18). Consequently, major intervention 4 can be performed at year 23, which would then be at a similar stage to that of an intervention in the baseline given the shift of processes. Finally, at year 30, interventions 1 and 3 can be introduced, which would be at a similar stage to that of an intervention in the baseline, but much less signiﬁcant than the full intervention 5. This, therefore, indicates that, within the optimized 30-year period under analysis, intervention 5 is not carried out. It must be noted that, over the full life cycle of the pavement, this intervention would be needed. However, by lengthening its life expectancy carrying out more preventative and correction intervention actions, intervention 5 has been shifted out of the assessment period. This means that the impacts, both ﬁnancially and environmentally, would be spread over a longer period. Considering the loading values and the inferences made from the gate activity, the optimized maintenance plan is given in Table 4. Table 4. Optimized maintenance action plans based on responses from piezo ﬂoating gate (PFG)-H3 sensors. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Optimized 0 0 0 0 0 1 0 0 0 2 0 0 0 1,3 0 0 0 0 0 1 0 0 0 4 0 0 0 0 0 0 1,3 Interventions 5.2. Optimized Plan Based on Asphalt Strain Gauges Response For the strain gauges utilized in the test section, similar trigger points were observed (Figures 9 and 10). Considering the trend shown by sensor T1 (Figure 9), the ﬁrst point to be considered as a trigger for intervention is at 207,600 loads (equivalent to four years). This point is based on the standard deviation of the max values shown from the sensor, which indicate that there is some damage is occurring within the pavement. The routine maintenance that was expected to be carried out at year 8 can be moved up to two years after the sensors have indicated that something is wrong, similar to the approach taken with the results from the PFG sensors. This is a year after the similar intervention is done with sensor H3. The next instance of noted change by the gauges is at an equivalent load of Infrastructures 2020, 5, 4 13 of 20 591,200 (equivalent to 12 years of the life cycle), which again is similar to the results of sensor H3. However, given that the ﬁrst noted change from the gauges is a year later than the PFG sensors, it can reasonably be assumed that the damage would have worsened further without any action, as opposed to the road monitoring scenario with the PFG sensor. This assumption is appropriate given the fact that it is the same pavement under analysis. Given this, an additional routine maintenance was inserted to ensure the condition is kept optimal. Therefore, routine maintenance intervention 1 was inserted at year 11 along with intervention 2 at year 10. Intervention 2 can be introduced midway between these planned interventions given the fact that the gauge is showing the distress is evolving, and this would enable its development to be reduced, prolonging the necessity of corrective maintenance. Subsequently, at year 15, intervention 1 and 3 can be introduced at the same point that intervention 4 was planned in the pre-set maintenance plan. A similar approach, as applied with the plan for the PFG sensors, for the interventions taking place between years 20–30 was also applied for the strain gauge. Therefore, the next intervention to account for is intervention 4 (shown at year 15 in the baseline). In the optimized approach, intervention 1 can be introduced at year 20 (given the inclusion of a thin overlay at year 15), which accounts for routine surface maintenance. This is also in line with the results from the PFG sensors indicating that, at a loading of 900,000 (equivalent to year 18), there are cracks appearing at the surface. Then, subsequent to this, major intervention 4 can be performed at year 24, which would then be at a similar stage to that of an intervention in the baseline given the shift of processes. Finally, at year 30, interventions 1 and 3 can be introduced, which would be at a similar stage to that of an intervention in the baseline, but much less than the full intervention 5. Considering the loading values and the inferences made from the threshold activity, the optimized maintenance plan is given in Table 5. Table 5. Optimized maintenance action plans based on responses from strain gauges. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Optimized 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 1,3 0 0 0 0 1 0 0 0 4 0 0 0 0 0 1,3 Interventions Comparable to the optimized plan for the sensors, intervention 5 is not carried out. Additionally, as previously indicated, within the full life cycle of pavement (which would now be more than the 30 years under analysis), this intervention would be needed. Similar to the plan for sensor H3, the impacts, both ﬁnancially and environmentally, would be allocated over a longer period, making the eect less per year. 6. The Use of Life Cycle Assessment Having identiﬁed dierent maintenance pipelines for each scenario, it was noted that, using the monitoring techniques, there are now more interventions occurring. They are of a lesser value in terms of work and ﬁnancial costs, but together they signify a larger number of interventions than in the pre-planned system. Therefore, the question arises as to whether using more interventions will cause more damage to the environment, even if they are extending the life of the pavement. Given the knowledge that the maintenance and rehabilitation of asphalt pavements can be quite environmentally impactful [35], the next step was to quantify their environmental impacts in terms of LCA indicators. 6.1. Goal and Scope Deﬁnition The study was performed in order for the environmental impacts of the three alternative product systems to be quantiﬁed and compared. The product systems include the maintenance phase of the same stretch of asphalt pavement. In the baseline scenario, the maintenance regime to be followed is one typical of usually implemented typical predeﬁned maintenance strategies. The two alternatives consist of optimized maintenance planning based upon the data acquired from the dierent types of sensing technologies that have been embedded in the corresponding pavement structures. The study Infrastructures 2020, 5, 4 14 of 20 thus aims to quantify the environmental burdens aliated with the maintenance pipeline of the two Infrastructures 2020, 5, x FOR PEER REVIEW 14 of 20 optimized scenarios and identify whether they exhibit an improvement compared with the baseline. 6.2. Functional Unit 6.2. Functional Unit The functional unit that was utilized for the specific study can be described as 1 m of the asphalt The functional unit that was utilized for the speciﬁc study can be described as 1 m of the asphalt pavement’s surface, along with the underlying asphaltic layers, providing adequate performance for pavement’s surface, along with the underlying asphaltic layers, providing adequate performance for a a period of 30 years. In detail, two bituminous layers are technically included and are both composed period of 30 years. In detail, two bituminous layers are technically included and are both composed by by the EME2 asphalt mixture, a high modulus mixture widely used in road engineering in France the EME2 asphalt mixture, a high modulus mixture widely used in road engineering in France [36]. [36]. The functional unit can be seen in Figure 12. The functional unit can be seen in Figure 12. Figure 12. Functional unit of the life cycle assessment (LCA) exercises. Figure 12. Functional unit of the life cycle assessment (LCA) exercises. 6.3. System Boundaries 6.3. System Boundaries Having deﬁned the functional unit, the next step was the deﬁnition of the system boundaries. Having defined the functional unit, the next step was the definition of the system boundaries. As deﬁned in dierent guidance documents [22,23,37], the four stages of an asphalt pavement’s life As defined in different guidance documents [22,23,37], the four stages of an asphalt pavement’s life cycle are the product stage, the construction stage, the use stage, and the end of life stage. In this study, cycle are the product stage, the construction stage, the use stage, and the end of life stage. In this the selected system boundaries are limited to the use stage and speciﬁcally to B2: maintenance, B3: study, the selected system boundaries are limited to the use stage and specifically to B2: maintenance, repair, and B4: replacement. The reason behind this methodological choice is the fact that the LCA B3: repair, and B4: replacement. The reason behind this methodological choice is the fact that the LCA exercises are conducted for the same pavement structure and the same analysis period of 30 years. exercises are conducted for the same pavement structure and the same analysis period of 30 years. Thus, the environmental impacts arising from the product, the construction, and the end of life stages Thus, the environmental impacts arising from the product, the construction, and the end of life stages would be the same. However, having optimized maintenance pipelines per alternative also indicates would be the same. However, having optimized maintenance pipelines per alternative also indicates that there are potential dierences in their environmental severity. The system boundaries for all three that there are potential differences in their environmental severity. The system boundaries for all alternatives can be seen in Figure 13. However, it is worth mentioning that, in the use phase (i.e., three alternatives can be seen in Figure 13. However, it is worth mentioning that, in the use phase maintenance, repair, replacement), the processes related to the raw materials needed to be extracted (i.e., maintenance, repair, replacement), the processes related to the raw materials needed to be in order for the maintenance to be achieved; their transport to the mixing plant and the construction extracted in order for the maintenance to be achieved; their transport to the mixing plant and the site, along with the production of products necessary for the maintenance of the pavements, were construction site, along with the production of products necessary for the maintenance of the included in the system boundaries. It should also be noted that more frequent interventions in the pavements, were included in the system boundaries. It should also be noted that more frequent road segment, even if of lower signiﬁcance/duration), could potentially impose higher costs for the interventions in the road segment, even if of lower significance/duration), could potentially impose users. However, as the analysis conducted in this study focuses only on the environmental pillar higher costs for the users. However, as the analysis conducted in this study focuses only on the of sustainability, the user/agency costs were not taken into consideration. Moreover, the required environmental pillar of sustainability, the user/agency costs were not taken into consideration. interventions now, in the optimized scenario, consist of mainly surface treatments and not structural Moreover, the required interventions now, in the optimized scenario, consist of mainly surface interventions. That would mean that, indeed, a higher amount of interventions would occur, but at the treatments and not structural interventions. That would mean that, indeed, a higher amount of same time, these interventions would cost, in terms of time, signiﬁcantly less. Finally, the environmental interventions would occur, but at the same time, these interventions would cost, in terms of time, impacts of these interventions have already been quantiﬁed, utilizing the maintenance itself as system significantly less. Finally, the environmental impacts of these interventions have already been boundaries. Environmental impacts related to trac congestion or rerouting were not taken into quantified, utilizing the maintenance itself as system boundaries. Environmental impacts related to consideration, based on the very deﬁnition of our product system. traffic congestion or rerouting were not taken into consideration, based on the very definition of our For the completion of the LCA case studies, Gabi ts, by Thinkstep [38], was utilized along product system. with the Gabi Professional and Ecoinvent 3 databases. Moreover, no primary data were acquired, For the completion of the LCA case studies, Gabi ts, by Thinkstep [38], was utilized along with and instead reputable data sources and literature, reports, international standards, product category the Gabi Professional and Ecoinvent 3 databases. Moreover, no primary data were acquired, and rules (PCRs), and environmental product declarations (EPDs) were used [22,23,37,39–43]. In terms instead reputable data sources and literature, reports, international standards, product category rules (PCRs), and environmental product declarations (EPDs) were used [22,23,37,39–43]. In terms of impact assessment methodology, ReCiPe 2016, Hierarchist (H) [44] was utilized for both Mid and Endpoint indicators. Infrastructures 2020, 5, 4 15 of 20 of impact assessment methodology, ReCiPe 2016, Hierarchist (H) [44] was utilized for both Mid and Endpoint indicators. Infrastructures 2020, 5, x FOR PEER REVIEW 15 of 20 Figure 13. Figure 13. Sy System stem bou boundaries ndaries of the of the produ product ct sy system. stem. 6.4. Life Cycle Assessment Results 6.4. Life Cycle Assessment Results As mentioned before, ReCiPe 2016 (H) was utilized as the impact assessment methodology in this As mentioned before, ReCiPe 2016 (H) was utilized as the impact assessment methodology in study. Thus, as proposed within the very same methodology and for reasons concerning the increased this study. Thus, as proposed within the very same methodology and for reasons concerning the accuracy of the study, the analysis of the LCA impact category indicators was conducted in two levels: increased accuracy of the study, the analysis of the LCA impact category indicators was conducted MidPoint and EndPoint. The midpoint indicators are associated with a wider range of analysis, while in two levels: MidPoint and EndPoint. The midpoint indicators are associated with a wider range of the endpoint impact category indicators correspond to three aggregated indicators, also characterized analysis, while the endpoint impact category indicators correspond to three aggregated indicators, as areas of protection (AOP). It is recommended for the two levels of analyses to be implemented in also characterized as areas of protection (AOP). It is recommended for the two levels of analyses to parallel, in order for more informed results to be acquired [44]. be implemented in parallel, in order for more informed results to be acquired [44]. 6.4.1. MidPoint Impact Category Indicators 6.4.1. MidPoint Impact Category Indicators Following the life cycle impact assessment and life cycle interpretation phases that were undertaken Following the life cycle impact assessment and life cycle interpretation phases that were for the realization of this study, the MidPoint impact category indicators can be found in Figure 14 for undertaken for the realization of this study, the MidPoint impact category indicators can be found in all three scenarios. This radar graph indicates the relative percentage variation of the values of the Figure 14 for all three scenarios. This radar graph indicates the relative percentage variation of the MidPoint impact category indicators compared with the baseline. The latter is depicted as the black values of the MidPoint impact category indicators compared with the baseline. The latter is depicted dashed line, while in red and green colors, the scenarios with the strain gauges and the PFG sensors as the black dashed line, while in red and green colors, the scenarios with the strain gauges and the can be found, respectively. PFG sensors can be found, respectively. From Figure 14, it can be seen that, in both the scenarios, where embedded sensing technologies From Figure 14, it can be seen that, in both the scenarios, where embedded sensing technologies were utilized, all the MidPoint impact category indicators exhibit lower values compared with the were utilized, all the MidPoint impact category indicators exhibit lower values compared with the baseline. This can be explained by the fact that, when the aforementioned sensing technologies are baseline. This can be explained by the fact that, when the aforementioned sensing technologies are utilized, the amount of available data about the condition of the pavement is signiﬁcantly higher utilized, the amount of available data about the condition of the pavement is significantly higher compared with situations where no sensors are utilized. Thus, more informed decisions regarding the compared with situations where no sensors are utilized. Thus, more informed decisions regarding way of selecting the most appropriate maintenance strategy and the most beneﬁcial time interval to the way of selecting the most appropriate maintenance strategy and the most beneficial time interval execute it can be achieved. Moreover, it can also be said that, when PFG sensors are used, the values of to execute it can be achieved. Moreover, it can also be said that, when PFG sensors are used, the the MidPoint indicators are slightly lower compared with the two other scenarios, because, with this values of the MidPoint indicators are slightly lower compared with the two other scenarios, because, with this technology, it became possible to acquire “warning data” concerning the condition of the pavement earlier that when conventional strain gauges were utilized. Infrastructures 2020, 5, 4 16 of 20 technology, it became possible to acquire “warning data” concerning the condition of the pavement earlier that when conventional strain gauges were utilized. Infrastructures 2020, 5, x FOR PEER REVIEW 16 of 20 Figure 14. Relative percentage variation of the MidPoint indicators’ values. Figure 14. Relative percentage variation of the MidPoint indicators’ values. 6.4.2. EndPoint Impact Category Indicators 6.4.2. EndPoint Impact Category Indicators The next step to be undertaken according to the ReCiPe 2016 (H) impact assessment The next step to be undertaken according to the ReCiPe 2016 (H) impact assessment methodology methodology is the quantification of the EndPoint impact category indicators. They are able to is the quantiﬁcation of the EndPoint impact category indicators. They are able to provide a broader provide a broader view of the impacts that the studied scenarios impose in the environment. In detail, view of the impacts that the studied scenarios impose in the environment. In detail, they allocate the they allocate the damages originating from the maintenance regimes of the three scenarios in three damages originating from the maintenance regimes of the three scenarios in three areas of protection; areas of protection; namely, damage to human health measured in disability-adjusted life years namely, damage to human health measured in disability-adjusted life years (DALY), damage to (DALY), damage to ecosystems expressed as time-integrated species loss, and damage to resource ecosystems expressed as time-integrated species loss, and damage to resource availability with surplus availability with surplus cost as its metric. In Figure 15, the values of the EndPoint impact category cost as its metric. In Figure 15, the values of the EndPoint impact category indicators per scenario indicators per scenario are displayed. are displayed. As Figure 15 depicts, the values of the Endpoint indicators are slightly reduced for both scenarios As Figure 15 depicts, the values of the Endpoint indicators are slightly reduced for both scenarios that utilize embedded sensing technologies. In detail, the values of the EndPoint indicators for the that utilize embedded sensing technologies. In detail, the values of the EndPoint indicators for the scenario with the PFG sensors are slightly reduced compared with those corresponding to the scenario with the PFG sensors are slightly reduced compared with those corresponding to the scenario scenario where conventional strain gauges were used. where conventional strain gauges were used. Infrastructures 2020, 5, 4 17 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 17 of 20 Figure 15. Values of the EndPoint impact category indicators per scenario. Figure 15. Values of the EndPoint impact category indicators per scenario. 7. Summary and Conclusions 7. Summary and Conclusions In this study, two embedded sensing technologies (PFG sensors and conventional asphalt In this study, two embedded sensing technologies (PFG sensors and conventional asphalt strain strain gauges) were used to develop optimized maintenance plans for road pavement management. gauges) were used to develop optimized maintenance plans for road pavement management. In In addition, in order to assess the environmental impact of the optimized approaches, a life cycle addition, in order to assess the environmental impact of the optimized approaches, a life cycle assessment (LCA) exercise was carried out. assessment (LCA) exercise was carried out. The sensors were located at the bottom of the asphalt layer of a section of the APT facility owned The sensors were located at the bottom of the asphalt layer of a section of the APT facility owned and managed by the IFSTTAR. Their responses were collected over the duration of the experiment and managed by the IFSTTAR. Their responses were collected over the duration of the experiment (three months) and analyzed in terms of maximum strain/voltage evolution with the increasing number (three months) and analyzed in terms of maximum strain/voltage evolution with the increasing of loads. The results show that both technologies depict the weakening of the pavement structure with number of loads. The results show that both technologies depict the weakening of the pavement strain and voltage measurements having a good correlation for the majority of the experiment. It was, structure with strain and voltage measurements having a good correlation for the majority of the however, noted that there were signiﬁcant dierences in the maximum microstrain measurements experiment. It was, however, noted that there were significant differences in the maximum from the strain gauges for the T1 and T2 gauges. This is important as it demonstrates that, for this microstrain measurements from the strain gauges for the T1 and T2 gauges. This is important as it technology, it is critical to have an array of devices employed to allow for critical evaluation and demonstrates that, for this technology, it is critical to have an array of devices employed to allow for accuracy by pavement engineers. This issue was not detected with the results of the PFG sensors, critical evaluation and accuracy by pavement engineers. This issue was not detected with the results as there was more consistency in the results of these sensors, and thus this represents an advantage of of the PFG sensors, as there was more consistency in the results of these sensors, and thus this utilizing this technology. Additionally, the newer version of piezoelectric sensors is wireless, which represents an advantage of utilizing this technology. Additionally, the newer version of piezoelectric decreases ﬁeld installation complexity and creates more ﬂexible data transmission protocols that can sensors is wireless, which decreases field installation complexity and creates more flexible data be associated with the strain gauges. Furthermore, piezoelectric sensors will be categorized as low-cost transmission protocols that can be associated with the strain gauges. Furthermore, piezoelectric solutions, which makes them more attractive not only for research projects, but for real case studies. sensors will be categorized as low-cost solutions, which makes them more attractive not only for Sensors T2 and H3 (Figure 10) suer a drastic change in slope at approximately 600,000 load research projects, but for real case studies. repetitions. After this point, deﬁned as critical, damage in the AC layer will grow at a much higher Sensors T2 and H3 (Figure 10) suffer a drastic change in slope at approximately 600,000 load rate, which may lead to structural failure if no maintenance is scheduled. The results from sensor repetitions. After this point, defined as critical, damage in the AC layer will grow at a much higher H3, Figure 11 (top), support this as the middle thresholds (3 and 4, Table 3) also become alive at rate, which may lead to structural failure if no maintenance is scheduled. The results from sensor H3, approximately 600,000 load repetitions. Finally, surface cracking is seen around 900,000 load repetitions, Figure 11 (top), support this as the middle thresholds (3 and 4, Table 3) also become alive at when all the thresholds have been wakened up, which validates the idea of using PFG sensors for approximately 600,000 load repetitions. Finally, surface cracking is seen around 900,000 load preventive pavement monitoring. repetitions, when all the thresholds have been wakened up, which validates the idea of using PFG sensors for preventive pavement monitoring. Infrastructures 2020, 5, 4 18 of 20 While the sensor results allowed for the generation of optimized pavement maintenance strategies, the LCA case study allowed for an evaluation of the environmental friendliness of these actions. The evaluation was done utilizing a commercial software, Gabi ts, and for life cycle impact assessment methodology, ReCiPe 2016 (H) was selected. Through this assessment, it was shown that both strategies presented environmental beneﬁts during the maintenance phase of the asphalt pavements, with the PFG sensors providing the greater beneﬁts of the two. It can thus be deduced that, when sensing technologies are embedded in the pavement structure and are able to provide the decision-makers with “in-time” information about the condition of the pavement, more informed choices can be made that lead to more environmentally friendly maintenance regimes, compared with the case when sensors are absent. A critical point in the analysis is that the optimized schedules allow for an extended life cycle of the pavement. This is done by carrying out more preventative maintenance interventions and not waiting on the more environmentally harmful corrective maintenance interventions when pavement failure has already occurred. These results present a pipeline for practical application of utilizing the embedded technologies within the pavement management industry. The use of the sensors has practical advantages given their embedded nature and, therefore, the lack of intrusiveness provided by the data collected from these sensors as compared with other technologies that require consistent physical intervening surveys. However, there is still more research to be done on these technologies given doubts with regards to the technologies’ life expectancy, the reliability of the results, and their end of life strategies. The failure of sensors needs to be further examined in future work as well, because once a sensor has suered failure, there is no easy solution given that they are embedded. Given these drawbacks to the technologies, this study helps to bridge these gaps by providing an understanding how to utilize the data from these sensors and how they can be interpreted for maintenance strategy formulation. It should also be noted that the cost of implementation of the sensors needs to be fully understood in future works, as this is a key component to practical implementation. More real-world experiments, similar to those in the study, will help validate and support the use of these technologies moving forward. Future developments should line up with the miniaturization of the technology, reducing complexity for ﬁeld deployments, and providing easy ways for visualization and management of the data. Author Contributions: Conceptualization, M.M.-P., R.R., M.B., and K.M.; methodology, M.M.-P., R.R., M.B., and K.M.; software, M.M.-P., R.R., and K.M.; validation, M.M.-P., R.R., M.B., and K.M.; formal analysis, M.M.-P., R.R., and K.M.; investigation, M.M.-P., R.R., M.B., and K.M.; resources, M.M.-P., R.R., M.B., and K.M.; data curation, M.M.-P., R.R., M.B., and K.M.; writing—original draft preparation, M.M.-P., R.R., and K.M.; writing—review and editing, M.M.-P., R.R., M.B., and K.M.; visualization, M.M.-P., R.R., and K.M. All authors have read and agreed to the published version of the manuscript. Funding: The research presented in this paper was carried out as part of the H2020-MSCA-ETN-2016. This project has received funding from the European Union’s H2020 Programme for research, technological development, and demonstration under grant agreement number 721493. Acknowledgments: The Accelerated Pavement Testing was carried out within the BioRePavation project. It was co-funded by Funding Partners of the ERA-NET plus Infravation and the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement number 607524. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Vandam, T.J.; Harvey, J.T.; Muench, S.T.; Smith, K.D.; Snyder, M.B.; Al-Qadi, I.L.; Ozer, H.; Meijer, J.; Ram, P.V.; Roesier, J.R.; et al. Towards Sustainable Pavement Systems: A Reference Document FHWA-HIF-15-002; Federal Highway Administration: Washington, DC, USA, 2015. 2. International Road Federation (IRF). IRF World Road Statistics 2018 (Data 2011–2016); International Road Federation (IRF): Brussels, Belgium, 2018. 3. Peterson, D. National Cooperative Highway Research Program Synthesis of Highway Practice Pavement Management Practices. No. 135; Transportation Research Board: Washington, DC, USA, 1987; ISBN 0309044197. Infrastructures 2020, 5, 4 19 of 20 4. Li, X.; Goldberg, D.W. Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urban Syst. 2018, 69, 51–62. [CrossRef] 5. Radopoulou, S.C.; Brilakis, I. Improving Road Asset Condition Monitoring. Transp. Res. Procedia 2016, 14, 3004–3012. [CrossRef] 6. Ragnoli, A.; De Blasiis, M.; Di Benedetto, A. Pavement Distress Detection Methods: A Review. Infrastructures 2018, 3, 58. [CrossRef] 7. Coenen, T.B.J.; Golroo, A. A review on automated pavement distress detection methods. Cogent Eng. 2017, 4, 1374822. [CrossRef] 8. Inzerillo, L.; Di Mino, G.; Roberts, R. Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Autom. Constr. 2018, 96, 457–469. [CrossRef] 9. Xue, W.; Wang, D.; Wang, L. A review and perspective about pavement monitoring. Int. J. Pavement Res. Technol. 2012, 5, 295–302. 10. Lajnef, N.; Rhimi, M.; Chatti, K.; Mhamdi, L.; Faridazar, F. Toward an integrated smart sensing system and data interpretation techniques for pavement fatigue monitoring. Comput. Civ. Infrastruct. Eng. 2011, 26, 513–523. [CrossRef] 11. Merenda, M.; Praticò, F.G.; Fedele, R.; Carotenuto, R.; Corte, F.G. Della A real-time decision platform for the management of structures and infrastructures. Electronics 2019, 8, 1180. [CrossRef] 12. Fedele, R.; Praticò, F.G.; Carotenuto, R.; Della Corte, F.G. Energy savings in transportation: Setting up an innovative SHM method. Math. Model. Eng. Probl. 2018, 5, 323–330. [CrossRef] 13. Hasni, H.; Alavi, A.H.; Chatti, K.; Lajnef, N. A self-powered surface sensing approach for detection of bottom-up cracking in asphalt concrete pavements: Theoretical/numerical modeling. Constr. Build. Mater. 2017, 144, 728–746. [CrossRef] 14. American Association of State Highway and Transportation Ocials (AASHTO). Pavement Management Guide; American Association of State Highway and Transportation Ocials (AASHTO): Washington, DC, USA, 2012. 15. Xue, W.; Wang, L.; Wang, D.; Druta, C. Pavement Health Monitoring System Based on an Embedded Sensing Network. J. Mater. Civ. Eng. 2014, 26, 04014072. [CrossRef] 16. Duong, N.S.; Blanc, J.; Hornych, P.; Bouveret, B.; Carroget, J.; Le feuvre, Y. Continuous strain monitoring of an instrumented pavement section. Int. J. Pavement Eng. 2018, 8436, 1435–1450. [CrossRef] 17. Gaborit, P.; Sauzéat, C.; Di Benedetto, H.; Pouget, S.; Olard, F.; Claude, A.; Monnet, A.J.; Audin, R.M.; Sauzéat, C.; Di Benedetto, H.; et al. Investigation of highway pavements using in-situ strain sensors. Int. Conf. Transp. Infrastruct. 2013, 28, 331. 18. Blanc, J.; Hornych, P.; Duong, N.S.; Blanchard, J.Y.; Nicollet, P. Monitoring of an experimental motorway section. Road Mater. Pavement Des. 2017, 20, 74–89. [CrossRef] 19. Stripple, H. Life Cycle Assessment of Road, A Pilot Study for Inventory Analysis; IVL RAPPORT. 1210; IVL: Linköping, Sweden, 2001; Volume 2. 20. Carlson, A. Life Cycle Assessment of Roads and Pavements—Studies Made in Europe; VTI Rapp. 736A; Statens väg-och transportforskningsinstitut: Linköping, Sweden, 2011; Volume 22. 21. ECRPD. Energy Conservation in Road Pavement Design, Maintenance and Utilisation. 2010, pp. 1–63. Available online: https://ec.europa.eu/energy/intelligent/projects/sites/iee-projects/ﬁles/projects/documents/ ecrpd_publishable_report_en.pdf (accessed on 29 December 2019). 22. The International Standards Organisation. Environmental Management—Life Cycle Assessment—Requirements and Guidelines; ISO 14044; The International Standards Organisation: Geneva, Switzerland, 2006; pp. 652–668. 23. International Organization for Standardization. ISO 14040-Environmental Management—Life Cycle Assessment—Principles and Framework; The International Standards Organisation: Geneva, Switzerland, 2006; Volume 3, p. 20. 24. Chappat, M.; Bilal, J. La route écologique du futur—Analyse du Cycle de Vie, Consommation d’énergie & émission de gaz à eet de serre. Développement Durable 2003, 40. 25. Häkkinen, T.; Mäkelä, K. Environmental Impact of Concrete and Asphalt Pavements; Technical Research Center of Finland: Espoo, Finland, 1996; ISBN 9513849074. 26. Mroueh, U.M.; Laine-Ylijoki, J.; Eskola, P. Life-cycle impacts of the use of industrial by-products in road and earth construction. Waste Manag. Ser. 2000, 1, 438–448. Infrastructures 2020, 5, 4 20 of 20 27. Hoang, T.; Jullien, A.; Ventura, A.; Crozet, Y. A global methodology for sustainable road—Application to the environmental assessment of French highway. In Proceedings of the 10 DBMC International Conference On Durability of Building Materials and Components, Lyon, France, 17–20 April 2005. 28. Tokyo Measuring Instruments Lab. TML Pam E-2020C Tranducers 2017. Available online: https://tml.jp/eng/ documents/Catalog/Transducers_E2020C.pdf (accessed on 12 November 2019). 29. Karim, C.; Alavi, A..; Hassene, H.; Lajnef, N.; Faridazar, F. Damage Detection in Pavement Structures Using Self-powered Sensors. In Proceedings of the 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements, Nantes, France, 6–9 June 2016; Springer: Berlin, Germany, 2016; pp. 665–671. 30. Lajnef, N.; Chatti, K.; Chakrabartty, S.; Rhimi, M.; Sarkar, P. Smart Pavement Monitoring System; Federal Highway Administration: Washington, DC, USA, 2013. 31. Alavi, A.H.; Hasni, H.; Lajnef, N.; Chatti, K.; Faridazar, F. An intelligent structural damage detection approach based on self-powered wireless sensor data. Autom. Constr. 2016, 62, 24–44. [CrossRef] 32. Loboratoire Central des Ponts et Chaussées. French Design Manual for Pavement Structures; Loboratoire Central des Ponts et Chaussées: Paris, France, 1997. 33. SETRA-LCPC. Catalogue des Structures Types de chaussées Neuves; SETRA-LCPC: Paris, France, 1998. 34. Laurent, G. Evaluation économique des chaussées en béton et classiques sur le réseau routier national français; ETUDES ET RECHERCHES DES LABORATOIRES DES PONTS ET CHAUSSEES-SERIE: ROUTES CR 33; ETUDES ET RECHERCHES DES LABORATOIRES DES PONTS ET CHAUSSEES: Paris, France, 2004. 35. Mantalovas, K.; Di Mino, G. The sustainability of reclaimed asphalt as a resource for road pavement management through a circular economic model. Sustainbility 2019, 11, 2234. [CrossRef] 36. Petho, L.; Denneman, E. Implementing EME2, the French high modulus asphalt in Australia. In Proceedings of the 6th Eurasphalt & Eurobitume Congress, Prague, Czech Republic, 1–3 June 2016. 37. EAPA. Guidance Document for Preparing Product Category Rules (Pcr) and Environmental Product Declarations (Epd) for Asphalt Mixtures; EAPA: Arlington County, VA, USA, 2017; p. 43. 38. Thinkstep AG Gabi ts 2019. Available online: https://www.thinkstep.com/content/gabi-databases-2019- edition (accessed on 29 December 2019). 39. The Norwegian EPD Foundation. Product-Category Rules (PCR) for Preparing an Environmental Product Declaration (EPD) for Product Group Asphalt and Crushed Stone; The Norwegian EPD Foundation: Skien, Norway, 2009. 40. EAPA. The Use of Warm Mix Asphalt; EAPA—Position paper; EAPA: Arlington County, VA, USA, 2014; p. 23. 41. BRE. Product Category Rules for Type III Environmental Product Declaration of Construction Products to EN 15804:2012; BRE Group: Watford, UK, 2013; p. 43. 42. European Bitumen Association. Life Cycle Inventory: Bitumen; European Bitumen Association: Harrogate, UK, 2012; ISBN 2930160268. 43. EC-European Commission. COMMISSION RECOMMENDATION of 9 April 2013 on the Use of Common Methods to Measure and Communicate the Life Cycle Environmental Performance; EC-European Commission: Brussels, Belgium, 2013; pp. 1–45. 44. Huijbregts, M.A.J.; Steinmann, Z.J.N.; Elshout, P.M.F.; Stam, G.; Verones, F.; Vieira, M.D.M.; Zijp, M.; van Zelm, R. ReCiPe 2016: A harmonized life cycle impact assessment method at midpoint and enpoint level—Report 1: Characterization. Natl. Inst. Public Health Environ. 2017, 22, 138–147. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Infrastructures Multidisciplinary Digital Publishing Institute http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/towards-more-sustainable-pavement-management-practices-using-embedded-cxwFgH4yuL

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References (40)

American Association of State Highway and Transportation Officials (AASHTO). Pavement Management Guide
(2003)
La route écologique du futur—Analyse du Cycle de Vie, Consommation d’énergie & émission de gaz à effet de serre
A. Alavi, Hassene Hasni, N. Lajnef, K. Chatti, Fred Faridazar (2016)
An intelligent structural damage detection approach based on self-powered wireless sensor data
Automation in Construction, 62
A. Carlson (2011)
Life cycle assessment of roads and pavements : studies made in Europe
on the Use of Common Methods to Measure and Communicate the Life Cycle Environmental Performance; EC-European Commission
T. Dam, J. Harvey, S. Muench, Kurt Smith, M. Snyder, I. Al-Qadi, H. Ozer, J. Meijer, Prashant Ram, J. Roesler, A. Kendall (2015)
Towards Sustainable Pavement Systems: A Reference Document
Antonella Ragnoli, M. Blasiis, A. Benedetto (2018)
Pavement Distress Detection Methods: A Review
Infrastructures
M. Merenda, F. Praticò, R. Fedele, R. Carotenuto, F. Corte (2019)
A Real-Time Decision Platform for the Management of Structures and Infrastructures
Electronics
TML Pam E - 2020 C Tranducers 2017
K. Mantalovas, G. Mino (2019)
The Sustainability of Reclaimed Asphalt as a Resource for Road Pavement Management through a Circular Economic Model
Sustainability
N. Duong, J. Blanc, P. Hornych, Benoît Bouveret, J. Carroget, Yann feuvre (2019)
Continuous strain monitoring of an instrumented pavement section
International Journal of Pavement Engineering, 20
M. Huijbregts, Zoran Steinmann, P. Elshout, Gea Stam, F. Verones, Marisa Vieira, A. Hollander, M. Zijp, R. Zelm (2016)
ReCiPe 2016. A harmonized life cycle impact assessment method at midpoint and endpoint level. Report 1: Characterization.
P. Gaborit, C. Sauzéat, H. Benedetto, S. Pouget, F. Olard, A. Claude (2014)
Investigation of highway pavements using in-situ strain sensors
Xiao Li, Daniel Goldberg (2018)
Toward a mobile crowdsensing system for road surface assessment
Comput. Environ. Urban Syst., 69
G. Laurent (2004)
Evaluation economique des chaussees en beton et classiques sur le reseau routier national francais
L. Inzerillo, G. Mino, R. Roberts (2018)
Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress
Automation in Construction
(1987)
National Cooperative Highway Research Program Synthesis of Highway Practice Pavement Management Practices
Wenjing Xue, Linbing Wang, Dong Wang, Christian Druta (2014)
Pavement Health Monitoring System Based on an Embedded Sensing Network
Journal of Materials in Civil Engineering, 26
(2017)
Document for Preparing Product Category Rules (Pcr) and Environmental Product Declarations (Epd) for Asphalt Mixtures
(2014)
The Use of Warm Mix Asphalt; EAPA-Position paper
Hassene Hasni, A. Alavi, K. Chatti, N. Lajnef (2017)
A self-powered surface sensing approach for detection of bottom-up cracking in asphalt concrete pavements: Theoretical/numerical modeling
Construction and Building Materials, 144
(2012)
Product Category Rules for Type III Environmental Product Declaration of Construction Products to EN 15804
T. Häkkinen, K. Mäkelä (1996)
Environmental adaption of concrete: Environmental impact of concrete and asphalt pavements
N. Lajnef, K. Chatti, S. Chakrabartty, M. Rhimi, P. Sarkar (2013)
Smart Pavement Monitoring System
(2001)
Life Cycle Assessment of Road, A Pilot Study for Inventory Analysis; IVL RAPPORT. 1210; IVL: Linköping
(2005)
A global methodology for sustainable road—Application to the environmental assessment of French highway
N. Lajnef, M. Rhimi, K. Chatti, L. Mhamdi, Fred Faridazar (2011)
Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring
Computer‐Aided Civil and Infrastructure Engineering, 26
(2006)
International Organization for Standardization. ISO 14040-Environmental Management-Life Cycle Assessment-Principles and Framework; The International Standards Organisation
K. Chatti, A. Alavi, Hassene Hasni, N. Lajnef, Fred Faridazar (2016)
Damage Detection in Pavement Structures Using Self-powered Sensors
, 13
J. Blanc, P. Hornych, N. Duong, J. Blanchard, Pierre Nicollet (2019)
Monitoring of an experimental motorway section
Road Materials and Pavement Design, 20
S. Radopoulou, I. Brilakis (2016)
Improving Road Asset Condition Monitoring
Transportation research procedia, 14
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Wenjing Xue, Dong Wang, Linbing Wang (2012)
A Review and Perspective about Pavement Monitoring
International journal of pavement research and technology, 5
J. Corté (1997)
FRENCH DESIGN MANUAL FOR PAVEMENT STRUCTURES
L. Petho, E. Denneman (2016)
Implementing EME2, the French high modulus asphalt in Australia
(1998)
Catalogue des Structures Types de chaussées Neuves
R. Fedele, F. Praticò, R. Carotenuto, F. Corte (2018)
Energy savings in transportation: Setting up an innovative SHM method
Mathematical Modelling of Engineering Problems
U. Mroueh, J. Laine-Ylijoki, P. Eskola (2000)
LIFE-CYCLE IMPACTS OF THE USE OF INDUSTRIAL BY-PRODUCTS IN ROAD AND EARTH CONSTRUCTION
Waste Management Series, 1
Conservation in Road Pavement Design, Maintenance and Utilisation
T. Coenen, A. Golroo (2017)
A review on automated pavement distress detection methods
Cogent Engineering, 4

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Abstract

infrastructures Article Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies 1 , 2 , 3 Mario Manosalvas-Paredes *, Ronald Roberts * , Maria Barriera and Konstantinos Mantalovas Nottingham Transportation Engineering Centre, University of Nottingham, Nottingham NG7 2RD, UK Dipartimento di Ingegneria, Scuola Politecnica, Ediﬁcio 8, Universitá degli Studi di Palermo, 90128 Palermo, Italy; konstantinos.mantalovas@unipa.it EIFFAGE Infrastructures GD—Research and Innovation Department, Corbas, CEDEX 09, 69960 Lyon, France; maria.barriera@eiage.com * Correspondence: ezzmam@nottingham.ac.uk (M.M.-P.); ronaldanthony.roberts@unipa.it (R.R.); Tel.: +44-7848-88-5919 (M.M.-P.); +39-348-442-8206 (R.R.) Received: 14 November 2019; Accepted: 26 December 2019; Published: 30 December 2019 Abstract: Road agencies are constantly being placed in dicult situations when making road maintenance and rehabilitation decisions as a result of diminishing road budgets and mounting environmental concerns for any chosen strategies. This has led practitioners to seek out new alternative and innovative ways of monitoring road conditions and planning maintenance routines. This paper considers the use of innovative piezo-ﬂoating gate (PFG) sensors and conventional strain gauges to continuously monitor the pavement condition and subsequently trigger maintenance activities. These technologies can help develop optimized maintenance strategies as opposed to traditional ad-hoc approaches, which often lead to poor decisions for road networks. To determine the environmental friendliness of these approaches, a case study was developed wherein a life cycle assessment (LCA) exercise was carried out. Observations from accelerated pavement testing over a period of three months were used to develop optimized maintenance plans. A base case is used as a guide for comparison to the optimized systems to establish the environmental impacts of changing the maintenance workﬂows with these approaches. On the basis of the results, the proposed methods have shown that they can, in fact, produce environmental beneﬁts when integrated within the pavement management maintenance system. Keywords: pavement management system; embedded sensors; piezoelectric sensors; accelerated pavement testing; life cycle assessment; environmental impact 1. Introduction 1.1. The Needs of Current Pavement Maintenance Systems and Practices In today’s global landscape, there are signiﬁcant challenges for development being faced by countries of all sizes and types. One of the primary concerns is the condition of the roadway network as this is a key determinant for development. This is because it is considered the gateway to mobility and access for citizens, which in turn leads to economic and social beneﬁts for the nation and its people [1]. This concern is further worsened by the continuing budget reductions for road authorities for pavement maintenance and rehabilitation programs [2]. These reductions result in authorities not having sucient ﬁnancial resources to maintain their networks in an optimal state. There are attempts to utilize optimization systems such as the pavement management system (PMS), which is based on utilizing the available ﬁnancial resources in the most ecient and valued Infrastructures 2020, 5, 4; doi:10.3390/infrastructures5010004 www.mdpi.com/journal/infrastructures Infrastructures 2020, 5, 4 2 of 20 manner based on the needs of the road network [3]. However, while the use of the PMS helps authorities to make optimized decisions, it is also highly dependent on the availability of data on the condition of the r Infrastructures oads in the 2020 network. , 5, x FOR PEER REVI The acquisition EW of this road condition data can be quite costly, as2 of the 20 most accurate technologies to date largely involve expensive equipment and vehicles featuring elements condition of the roads in the network. The acquisition of this road condition data can be quite costly, such as laser proﬁlers [4]. These systems in many cases require signiﬁcant time and substantial training as the most accurate technologies to date largely involve expensive equipment and vehicles featuring for the authorized personnel working in the road authorities. As a result of this, agencies quite often elements such as laser profilers [4]. These systems in many cases require significant time and rely on the use of manual surveys to obtain this data [5] and, as a result, these data can be considered substantial training for the authorized personnel working in the road authorities. As a result of this, subjective and, in some cases, inaccurate. This leads to the development of poor maintenance strategies agencies quite often rely on the use of manual surveys to obtain this data [5] and, as a result, these or, in many cases, a pre-set strategy with no capacity to adapt to real-time circumstances and challenges. data can be considered subjective and, in some cases, inaccurate. This leads to the development of To this end, it has been the goal of many authorities and agencies to ﬁnd lower-cost solutions for poor maintenance strategies or, in many cases, a pre-set strategy with no capacity to adapt to real- monitoring road conditions for the purpose of building accurate and robust asset databases. time circumstances and challenges. To this end, it has been the goal of many authorities and agencies There are several dierent areas of research in this domain [6,7]. Generally, the two most researched to find lower-cost solutions for monitoring road conditions for the purpose of building accurate and robust asset databases. areas of study of automated pavement distress collection systems are the ones based on systems There are several different areas of research in this domain [6,7]. Generally, the two most utilizing lasers and imaging technologies [8]. There are advantages and disadvantages of both of researched areas of study of automated pavement distress collection systems are the ones based on these systems, with the laser-based systems generally being more accurate, but the imaged-based ones systems utilizing lasers and imaging technologies [8]. There are advantages and disadvantages of carrying a lower cost. However, with both systems, there is still a need for continuous physical surveys both of these systems, with the laser-based systems generally being more accurate, but the imaged- to be carried out on the road to inspect conditions. based ones carrying a lower cost. However, with both systems, there is still a need for continuous Apart from the aforementioned technologies, there is signiﬁcant research built around the use physical surveys to be carried out on the road to inspect conditions. of in situ monitoring systems for the acquisition of accurate information concerning the conditions Apart from the aforementioned technologies, there is significant research built around the use of the pavement. Such systems allow documenting the level of service of the road asset via the use of in situ monitoring systems for the acquisition of accurate information concerning the conditions of of embedded sensors and technologies, which can allow for remote and continuous monitoring over the pavement. Such systems allow documenting the level of service of the road asset via the use of the life cycle of the pavement for fatigue [9–13]. Such systems do not require frequent surveys and embedded sensors and technologies, which can allow for remote and continuous monitoring over the state of the pavements can be monitored without any disruption to the trac or road network, the life cycle of the pavement for fatigue [9–13]. Such systems do not require frequent surveys and which the sta is an te of advantage the pavements over the can be moni systems mentioned tored without befor any di e. These sruption to the tra embedded fsystems fic or roatypically d networkwork , which is an advantage over the systems mentioned before. These embedded systems typically work by monitoring strains of the asphaltic layers, which can then be interpreted to help road agencies to by monitoring strains of the asphaltic layers, which can then be interpreted to help road agencies to discern information on the condition of the pavement. Accurate post-processing and analysis of data discern information on the condition of the pavement. Accurate post-processing and analysis of data coming from the sensors are fundamental in order to deﬁne an adapted and cost-eective management coming from the sensors are fundamental in order to define an adapted and cost-effective plan. The information can be commonly utilized within a PMS for particular needs, as shown in management plan. The information can be commonly utilized within a PMS for particular needs, as Figure 1 below [14]. The full extent to which the information can be utilized within the PMS is not shown in Figure 1 below [14]. The full extent to which the information can be utilized within the PMS covered within this study, but the ways in which the data collected can be used for determining needs is not covered within this study, but the ways in which the data collected can be used for determining of the pavement and for planning interventions are the main focus of the work. Furthermore, the data needs of the pavement and for planning interventions are the main focus of the work. Furthermore, obtained through the sensors can be considered under information quality level 4, which considers the the data obtained through the sensors can be considered under information quality level 4, which structure and condition of the pavement for planning and performance evaluation. considers the structure and condition of the pavement for planning and performance evaluation. Determining needs of road Addressing impacts of different strategies (What If scenarios) Providing project and treatment recomendations Allocation of funds Setting performance targets Long-term planning Communication of current network conditions Figure Figure 1. 1. Typi c Typical uses of pavement manage al uses of pavement managemen ment info t informa rmation in a pavement managemen tion in a pavement management s ty system stem (PMS). (PMS). Infrastructures 2020, 5, 4 3 of 20 The use of the data can, therefore, extend the road service life and improve its safety. For the purpose of this paper, conventional strain gauges and piezoelectric sensors are considered for monitoring road conditions, and consequently triggering maintenance activities. An insight into their use is explored and the possibilities of their use in road condition monitoring are identiﬁed through an experimental case study. 1.2. Environmental Concerns about Employing New Detection Systems With the use of these embedded technologies, it is possible for early detection of pavement distresses and for preventative maintenance to be employed instead of the costlier corrective maintenance practices that would be needed once the pavement would have already failed [15–18]. In scenarios typical in small authorities, there is usually a pre-set maintenance plan for the life cycle of the pavement based on the experience of the area and the available funds. With the use of these types of maintenance plans, there is no customization based on the real conditions of the roads. Therefore, limited preventative maintenance is done to help lengthen the pavement life cycle and save money for the authority. They essentially operate on the ‘worst-ﬁrst’ approach, wherein the pavements are allowed to reach their failure point without any preventative measures deployed [1]. As a result, the use of embedded sensors would be a welcome addition for the authority. However, the use of these proactive sensors can result in more frequent maintenance interventions as more preventative interventions would be utilized based on the triggers of the sensors to delay the use of the more costly corrective maintenance interventions when the pavement has suered both functional and structural failure. This result brings into question the environmental friendliness of using these approaches, as more frequent interventions can have more severe environmental impacts. Transportation can be quite energy-intensive, and thus the associated environmental impacts can be adverse. In many cases, however, the construction, operation, and maintenance of the road pavements or road networks have been considered less signiﬁcant in terms of environmental impacts, when compared with the potential environmental impacts by the vehicles utilizing the speciﬁc road or road network during its life cycle [19,20]. Given the need for more sustainable transportation infrastructures and asphalt pavements, it has lately become apparent that aspects such as the road construction and maintenance could lead to signiﬁcantly increased amounts of energy consumed, and hence to higher amounts of emissions [19,21]. To further investigate, the aforementioned environmental implications the life cycle assessment methodology can be utilized as described in international standards [22,23]. This study, however, has as a main objective to compare the environmental impacts of three dierent alternatives, namely, three dierent maintenance pipelines, focusing only on the use phase of the asphalt road and speciﬁcally on its maintenance. Numerous studies have been conducted so far that assess the environmental impacts of asphalt pavements over their life cycle. For instance, Häkkinen & Mäkele assessed the environmental impacts of the pavement construction, maintenance, and trac, followed by Chappat and Bilal, who also focused on the same aspects of the environmental assessment of a road [24,25]. Other researchers also included the environmental impacts arising owing to the construction of the necessary earthworks, surrounding a road pavement [19,26], while Hoang et al. only assessed the environmental impacts of the road construction and maintenance [27]. It thus becomes evident that the use of life cycle assessment for roads is strongly correlated with the objective of the study and can be implemented. In the speciﬁc investigation, as it has a comparative nature, the comparison is undertaken with the same pavement structure. However, with alternative maintenance strategies each time, the stage of pavement construction, the impacts of the earthworks’ construction, the trac impacts, and the end of life were omitted from the study. This is because of the fact that a comparative study would not beneﬁt from the inclusion of identical aspects in all the alternatives. In other words, the omitted aspects would have no inﬂuence on the outcomes of the study. Infrastructures 2020, 5, 4 4 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 4 of 20 1.3. Aim of the Study 1.3. Aim of the Study This paper carried out a life cycle assessment (LCA) case study to quantify the environmental This paper carried out a life cycle assessment (LCA) case study to quantify the environmental impacts of the maintenance pipelines based on three dierent scenarios (technologies). The baseline impacts of the maintenance pipelines based on three different scenarios (technologies). The baseline scenario is where no sensors are embedded in the pavement structure, and thus a preset maintenance scenario is where no sensors are embedded in the pavement structure, and thus a preset maintenance plan is followed; the second scenario utilizes piezo-electric sensors and the third utilizes conventional plan is followed; the second scenario utilizes piezo-electric sensors and the third utilizes conventional strain gauges, in order for optimized maintenance pipelines to be achieved. The LCA was carried out strain gauges, in order for optimized maintenance pipelines to be achieved. The LCA was carried out using results from the experimental test section, where the sensors were deployed in an accelerated using results from the experimental test section, where the sensors were deployed in an accelerated pavement testing setup. Environmental impacts of devising maintenance plans based on intervention pavement testing setup. Environmental impacts of devising maintenance plans based on intervention triggers from the sensors as opposed to a typical pre-set plan were compared and analyzed. triggers from the sensors as opposed to a typical pre-set plan were compared and analyzed. 1.4. Structure of the Study 1.4. Structure of the Study Before the LCA case study could be done, it was also very important to understand how the Before the LCA case study could be done, it was also very important to understand how the sensor results are read and interpreted, as this will establish the practicality of using them in real-world sensor results are read and interpreted, as this will establish the practicality of using them in real- conditions. To this end, Section 2 describes the gauges and sensors utilized in the study and Section 3 world conditions. To this end, Section 2 describes the gauges and sensors utilized in the study and explains the experimental setup of the study. The results of the sensors in the case study are then Section 3 explains the experimental setup of the study. The results of the sensors in the case study are provided in Section 4, detailing how results are read and analyzed, whereas Section 5 deals with the then provided in Section 4, detailing how results are read and analyzed, whereas Section 5 deals with formulation of the maintenance strategies. Finally, the results of the LCA are provided in Section 6, the formulation of the maintenance strategies. Finally, the results of the LCA are provided in Section with further discussions being made on the results of the LCA and the use of the embedded sensors. 6, with further discussions being made on the results of the LCA and the use of the embedded sensors. 2. Embedded Sensors Considered in the Study 2. Embedded Sensors Considered in the Study 2.1. Strain Gauges 2.1. Strain Gauges This study used a strain gauge denoted as type KM-100HAS, provided by TML (Tokyo Measuring Instruments This study Laboratory used aCo., stra Ltd.—T in gau okyo ge denot Sokkied Kenkyujo) as type (Figur KM-10 e0 2H ).A This S, provided device is b waterpr y TML oof, (To and kyo it Measur is designed ing In tostruments withstandLaboratory Co high temperatur ., Ltd.—To es and compaction kyo Sokki Kenk loads,yujo) usually (Figure associated 2). This d withe asphalt vice is pavement waterproof constr , and uction it is [des 28].igned t Strainsoar w e isensed thstand h when igh the teﬂanges mperatures are slightly and com varied pacby tion lo strains ads, generated usually a inside ssociathe ted with a asphaltsphal and small t pavement constructi displacements ar on [28 e transferr ]. Strain ed s are se to the spring nsed when the flange element. Asphalt s are strains slightly are va then ried by strai convertedninto s genera electrical ted insignals side the and aspha read lt a out nd by sma all d data isp acquisition lacements are tr system. ans T fypical erred to the solutions spring for element. the transmission Asphalt of strains the data are then conver use externalted into e boxes forlec storing trical s the igncables als and re (wirad o es) and ut by a the d data ata ac acquisition quisition system. Typ system. These ical boxes solutions can be for the trans located atm the issr ion of th oadsidee data and can use ex be power ternal boxes ed by solar for storing the panels, allowing cables (wire data transmission s) and the dto ata a acq cloud uisit server ion syst . The em transducer . These bo has xes an caappar n be lo ent cat elastic ed at modulus the roads ofide appr and oximately can be powered by 40 N/mm , r esistance solar panels, of 350 allow ohming (W)data tran full bridge, smis rated sion to a clo output appr ud serve oximately r. Thof e trans 2.5 mV duce /V,rcapacity has an a of ppa 5000 rent ela 10 stic strain, modulus of a and a temperatur pproximately 4 e range 0 N/m between m , resi20 stan Cce of and 35 180 0 ohm C. Equation (Ω) full brid (1) shows ge, rat the ed −6 output calculation approxi method mately of 2.5 when variation mV/V, ca in temperatur pacity of ±50 e is 00 ignor × 10ed, strain, whereand " corr a temper esponds ature to r the ange strain between value 6 6 6 (− 20 10 °C ),aC nd to 18 the 0 °C. Equa calibration tion (1 coe ) cient shows the cal (10 /1c 10 ulat), ion method and " to the when va measurri ed atchange ion in tempera from theture i initial s " i −6 −6 −6 value ignore(d,10 wher ) considering e ε1 corresponds to the stra a gauge factoriof n v two. alue (×10 ), Cε to the calibration coefficient (10 /1 × 10 ), −6 and εi to the measured change from the initial value (×10 ) considering a gauge factor of two. " = C " (1) 1 " i 𝜀 =𝐶 ∗𝜀 (1) Figure 2. Figure 2. Tokyo Meas Tokyo Measuring uring I Instr nstruments uments L Lab ab (TML (TML) ) strain strain g gauge. auge. Infrastructures 2020, 5, 4 5 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 5 of 20 2.2. Piezoelectric Sensors 2.2. Piezoelectric Sensors Piezoelectric sensors have become more popular in strain and vibration sensing owing to their Piezoelectric sensors have become more popular in strain and vibration sensing owing to their ability to harvest mechanical energy from ambient variations [10,29]. Recent research has shown that ability to harvest mechanical energy from ambient variations [10,29]. Recent research has shown that piezoelectric transducers can be used to self-power sensors for long-term monitoring applications [13]. piezoelectric transducers can be used to self-power sensors for long-term monitoring applications Under trac loading, piezoelectric sensors harvest the induced micro-strain energy in the asphalt [13]. Under traffic loading, piezoelectric sensors harvest the induced micro-strain energy in the concrete (AC) layer to power the sensor electronics and to assess the pavement condition. In this asphalt concrete (AC) layer to power the sensor electronics and to assess the pavement condition. In study, a rectangular polyvinylidene ﬂuoride (PVDF) ﬁlm is used to convert strain energy into an this study, a rectangular polyvinylidene fluoride (PVDF) film is used to convert strain energy into an electrical signal. The open source voltage (V) generated by a PVDF ceramic transducer material can be electrical signal. The open source voltage (V) generated by a PVDF ceramic transducer material can calculated using Equation (2), where S, Y, d , h, and ", are the applied strain, Young’s modulus of be calculated using Equation (2), where S, Y, d31, h, and ε, are the applied strain, Young’s modulus of the piezoelectric material, piezoelectric constant, thickness, and electrical permittivity, respectively. the piezoelectric material, piezoelectric constant, thickness, and electrical permittivity, respectively. The generated energy (E ) from a piezoelectric transducer across a load resistance (R) is shown in The generated energy (En) from a piezoelectric transducer across a load resistance (R) is shown in Equation (3), where t is the loading time. Equation (3), where f tf is the loading time. 𝑆 𝑌 𝑑 ℎ S Y d h (2) 𝑉= V = (2) 𝑉 𝑡 𝐸 = 𝑑𝑡 (3) ( ) V t E = dt (3) For the purpose of this study, a particular type of piezoelectric sensor was utilized—the recently For the purpose of this study, a particular type of piezoelectric sensor was utilized—the recently developed piezo-floating-gate (PFG) sensor, Figure 3. This sensor is equipped with a series of developed piezo-ﬂoating-gate (PFG) sensor, Figure 3. This sensor is equipped with a series of memory memory cells that successively store the duration of strain events. The PFG sensor starts measuring cells that successively store the duration of strain events. The PFG sensor starts measuring when when the amplitude of the input signal, coming from the piezoelectric transducer, exceeds one or the amplitude of the input signal, coming from the piezoelectric transducer, exceeds one or more more threshold [30,31]. The piezoelectric sensor can incorporate an antenna for direct (wireless) data threshold [30,31]. The piezoelectric sensor can incorporate an antenna for direct (wireless) data transmission. A reader (in a form of a USB) then communicates with the sensor through a specific transmission. A reader (in a form of a USB) then communicates with the sensor through a speciﬁc radio radio frequency; initial experiments have shown that data transmission could be done up to a frequency; initial experiments have shown that data transmission could be done up to a maximum car maximum car speed of 70 km/h. speed of 70 km/h. Figure 3. Piezo ﬂoating gate (PFG) sensor composition. PVDF, polyvinylidene ﬂuoride. Figure 3. Piezo floating gate (PFG) sensor composition. PVDF, polyvinylidene fluoride. Sensor results can be characterized by the following cumulative distribution (CDF) function, Sensor results can be characterized by the following cumulative distribution (CDF) function, Equation (4), where is the mean of the deformation distribution, is the standard deviation Equation (4), where μ is the mean of the deformation distribution, σ is the standard deviation considering load and frequency variability, and is the total cumulative time of the applied strain. considering load and frequency variability, and α is the total cumulative time of the applied strain. The statistical parameters and of the deformation distribution can be considered as indicators of The statistical parameters μ and σ of the deformation distribution can be considered as indicators of damage progression. In fact, and are the only viable tools to analyze the results delivered by the damage progression. In fact, μ and σ are the only viable tools to analyze the results delivered by the PFG sensor. These parameters are obtained by means of a curve adjustment of the sensor distribution PFG sensor. These parameters are obtained by means of a curve adjustment of the sensor distribution results taken from the memory cells (D1–D7) [31]. results taken from the memory cells (D1–D7) [31]. " !# (" ) 𝛼 𝜀 𝜇 F(") = 1 er f (4) 𝐹 𝜀 = 1 𝑒 (4) 𝜎 2 𝑟𝑓 Infrastructures 2020, 5, 4 6 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 20 3. Experimental Test Section 3. Experimental Test Section 3. Experimental Test Section To analy To analy ze th ze th e use e use of the of the aforementione aforementione d techno d techno log log iei se , s an exp , an exp ere im rim ent ent al al set set up u w p w as orch as orch est est rat rat ed e d To analyze the use of the aforementioned technologies, an experimental setup was orchestrated wherein the technologies were set up in a situation simulating real-world conditions. The experiment wherein the technologies were set up in a situation simulating real-world conditions. The experiment wherein the technologies were set up in a situation simulating real-world conditions. The experiment was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which was carried out in a section of approximately 32 m in length of the fatigue carrousel, Figure 4, which is an accelerated pavement testing (APT) facility owned and managed by The French Institute of is an accelerated pavement testing (APT) facility owned and managed by The French Institute of is an accelerated pavement testing (APT) facility owned and managed by The French Institute of Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies Science and Technology for Transport, Development, and Networks (IFSTTAR). Both technologies were powered by an external data center owned and operated by IFSTTAR. This setup also allowed were powered by an external data center owned and operated by IFSTTAR. This setup also allowed were powered by an external data center owned and operated by IFSTTAR. This setup also allowed for for the transmission of data during the experiment. Within Figure 4, the precise location of the sensors for the transmission of data during the experiment. Within Figure 4, the precise location of the sensors the transmission of data during the experiment. Within Figure 4, the precise location of the sensors is is shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four is shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four shown, wherein T1 and T2 are the strain gauges and H3–H8 are the piezoelectric sensors. The four arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to half arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to half arms of the APT were equipped with standard dual wheels of 65 kilo-Newton (kN) equivalent to of the standard French equivalent axle load [32]. The loading program began on 14 November 2017 of the standard French equivalent axle load [32]. The loading program began on 14 November 2017 half of the standard French equivalent axle load [32]. The loading program began on 14 November and finished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an and finished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an 2017 and ﬁnished on 15 February 2018, where a total of 999,200 repetitive loads were applied with an approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). approximate velocity of 76 km/h, corresponding to 10.0 rounds per minute (0.1667 Hertz). Figure 4. Distribution of sensors within the pavement structure. Figure Figure 4. 4. Distribution Distribution of s of sensors ensors within the within the pavement pavement stru structur ctue. re. The tested pavement structure is ﬂexible by nature and comprised of 10 cm of bituminous surface The tested pavement structure is flexible by nature and comprised of 10 cm of bituminous The tested pavement structure is flexible by nature and comprised of 10 cm of bituminous and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The structure of surface and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The surface and binder courses, and 76 cm of unbound granular base laid upon the subgrade. The the pavement is depicted in Figure 5. The pavement conﬁguration was done given typical testing structure of the pavement is depicted in Figure 5. The pavement configuration was done given typical structure of the pavement is depicted in Figure 5. The pavement configuration was done given typical conditions for testing new materials and equipment by IFFSTAR. testing conditions for testing new materials and equipment by IFFSTAR. testing conditions for testing new materials and equipment by IFFSTAR. Figure 5. Structure of pavement used in the test section. Figure 5. Structure of pavement used in the test section. Figure 5. Structure of pavement used in the test section. The section was instrumented with two horizontal strain gages placed at the bottom of the The section was instrumented with two horizontal strain gages placed at the bottom of the asphalt The section was instrumented with two horizontal strain gages placed at the bottom of the layer asphin altthe layer longitudinal in the long diirtection, udinal d asirec well tion, as w as six PFG ell as s sensors ix (ﬁve PFG sensors in the longitudinal (five in thdir e long ection itudin andal asphalt layer in the longitudinal direction, as well as six PFG sensors (five in the longitudinal direction and one in the transverse direction), as shown in Figure 4. The above-mentioned sensors one in the transverse direction), as shown in Figure 4. The above-mentioned sensors were placed at direction and one in the transverse direction), as shown in Figure 4. The above-mentioned sensors the were placed center of the at the center APT wheelof the APT path. The w radius heel p of ath. Th the cire rad cularius o wheel f the circ path exhibits ular wh aeel p radius ath exhibits a of 19.0 m. were placed at the center of the APT wheel path. The radius of the circular wheel path exhibits a radius of 19.0 m. Temperature variations in the asphalt layer were recorded at the top, middle, and Temperature variations in the asphalt layer were recorded at the top, middle, and bottom of the AC. radius of 19.0 m. Temperature variations in the asphalt layer were recorded at the top, middle, and bottom of the AC. bottom of the AC. Infrastructures 2020, 5, 4 7 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 7 of 20 Temperature measurements were performed at time intervals of 10 min over the duration of the Temperature measurements were performed at time intervals of 10 min over the duration of the test. Strain gages and PFG sensors measurements were recorded at approximately every 20,000 load test. Strain gages and PFG sensors measurements were recorded at approximately every 20,000 load repetitions. Figure 6 shows the responses from sensors T1 and H3 after 5000 load repetitions (1250 cycles). repetitions. Figure 6 shows the responses from sensors T1 and H3 after 5000 load repetitions (1250 This loading was based on sensor survivability tests carried out to ensure all the sensors utilized in the cycles). This loading was based on sensor survivability tests carried out to ensure all the sensors study were still alive. As can be seen, the sensors clearly respond to the passing arms of the carousel. utilized in the study were still alive. As can be seen, the sensors clearly respond to the passing arms The maximum strain (from TML sensors) and voltage (from PFG sensors) were considered for the of the carousel. The maximum strain (from TML sensors) and voltage (from PFG sensors) were purposes of this paper. For the purpose of applying the sensors in real-world conditions, the trac considered for the purposes of this paper. For the purpose of applying the sensors in real-world wander, and particularly the wheel wander, are important to ensure sensors are being deployed conditions, the traffic wander, and particularly the wheel wander, are important to ensure sensors along the correct paths [30]. Wandering distribution is studied through eleven positions, equidistant are being deployed along the correct paths [30]. Wandering distribution is studied through eleven every 0.11 m. Position one is at 18.48 m from the center of the carousel, while position eleven is at positions, equidistant every 0.11 m. Position one is at 18.48 m from the center of the carousel, while 19.53 m. The loading program followed a Gaussian distribution, where position six located at radius position eleven is at 19.53 m. The loading program followed a Gaussian distribution, where position 19.0 m supports 22.0% of the total loads and positions from one to ﬁve supported 1%, 3%, 7%, 11%, six located at radius 19.0 m supports 22.0% of the total loads and positions from one to five supported and 17% respectively. 1%, 3%, 7%, 11%, and 17% respectively. Figure Figure 6. 6. Measur Measurement from sensors T1 ement from sensors T1 (TM (TML) L) ( (top top)) and and sensors sensors H3 H3 (PF (PFG) G) ( (bottom bottom), ), after after 5000 5000 loads. loads. 4. Sensor Results 4. Sensor Results To allow for the application of the sensors in practical ﬁelds, it is important to understand how To allow for the application of the sensors in practical fields, it is important to understand how they generally function and the types of results that are generated from the sensors for dierent load they generally function and the types of results that are generated from the sensors for different load responses. To this end, Figure 7 shows the average longitudinal strain response from both TML responses. To this end, Figure 7 shows the average longitudinal strain response from both TML sensors during the APT experiment, where a clear dierence in terms of the maximum value is seen. sensors during the APT experiment, where a clear difference in terms of the maximum value is seen. This highlights how the maximum values utilized for this study were ascertained. Figure 7 (left) shows the responses from T1 where a constantly increasing trend occurs until 591,200 load Infrastructures 2020, 5, 4 8 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 20 This highlights how the maximum values utilized for this study were ascertained. Figure 7 (left) shows Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 20 the responses from T1 where a constantly increasing trend occurs until 591,200 load applications where applications where its maximum value, 123.3 microstrain (µs), is reached. On the other hand, Figure its maximum value, 123.3 microstrain (s), is reached. On the other hand, Figure 7 (right) shows 7 (right) shows the responses from T2 where its maximum value, 1446.7 µs, is reached at 820,000 load applications where its maximum value, 123.3 microstrain (µs), is reached. On the other hand, Figure the responses from T2 where its maximum value, 1446.7 s, is reached at 820,000 load repetitions. repetitions. It 7 (right) show is worth mentioning how s the responses from T2 w the co here its mpression–tension maximum value, 14 –c 46 ompressi .7 µs, is rea on c(CTC) cycle c hed at 820,000 l ho ange ad s It is worth mentioning how the compression–tension–compression (CTC) cycle changes between the between the two TML repetitions. It is worth mentioning how gauges. Table 1 summar the coimpression–tension zes the CTC cycle –c and F ompressi igureon 7 d (CTC) cycle c epicts how the sh hangeape s two TML gauges. Table 1 summarizes the CTC cycle and Figure 7 depicts how the shape of the between the two TML gauges. Table 1 summarizes the CTC cycle and Figure 7 depicts how the shape of the longitudinal strain changes with the increasing number of loads. It is known that the shape longitudinal of the long strain itudina changes l strain c with hange the s wit incrh easing the incre number asing number o of loads. Itf l is oads known . It is that known t the shape hat th will e shape not be will not be entirely symmetrical because of the viscoelastic behavior of the asphalt concrete (AC); entir wi ely ll not be enti symmetrical rely because symmetri of cal the because of viscoelastic the vi behavior scoela of sti the c beha asphalt vior of concr the a etes(AC); phalt concrete nonetheless, (AC); it is nonetheless, it is observed for both TML sensors how the CTC decrease/decrease/increase with nonetheless, it is observed for both TML sensors how the CTC decrease/decrease/increase with observed for both TML sensors how the CTC decrease/decrease/increase with damage. damage. damage. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Figure 7. Average gauge signal for TML–T1 and TML–T2, respectively, with characteristic points. Table 1. Maximum and minimum values for longitudinal strain gauges with load applications. TML, Tokyo Measuring Instruments Lab. Table 1. Table 1. Maximum a Maximum a nd n minimum values d minimum values for longitud for longitudinal st inal strain gau rain gau g g es wi es wi th load applic th load applic ata ions. TM tions. TM L, L, Tokyo Measuring Instruments Lab. Tokyo Measuring Instruments Lab. TML–T1 TML–T2 Load Applications TML–T1 TML–T2 TML–T1 TML–T2 Min-1 Max Min-2 Min-1 Max Min-2 Load Applications Load Applications Min-1 Max Min-2 Min-1 Max Min-2 Min-1 Max Min-2 Min-1 Max Min-2 5000 22.7 87.7 16.2 21.6 105.5 13.3 5000 −22.7 87.7 −16.2 −21.6 105.5 −13.3 5000 −22.7 87.7 −16.2 −21.6 105.5 −13.3 591,200 18.6 123.3 28.2 64.8 641.6 49.6 591,200 −18.6 123.3 −28.2 −64.8 641.6 −49.6 820,000 N/A N/A N/A 161.9 1446.7 190.2 591,200 −18.6 123.3 −28.2 −64.8 641.6 −49.6 820,000 N/A N/A N/A −161.9 1446.7 −190.2 999,200 13.5 13.2 55.8 72.1 309.4 364.8 820,000 N/A N/A N/A −161.9 1446.7 −190.2 999,200 −13.5 13.2 −55.8 −72.1 309.4 −364.8 999,200 −13.5 13.2 −55.8 −72.1 309.4 −364.8 It was also noted that there is an eect caused by the movement of the load on the surface. This is It was also noted that there is an effect caused by the movement of the load on the surface. This It was also noted that there is an effect caused by the movement of the load on the surface. This important as this must be considered when identifying the location for the installation of the sensors. is important as this must be considered when identifying the location for the installation of the is important as this must be considered when identifying the location for the installation of the Figure 8 shows the eect of distributing the load on the surface, wandering, where it is seen how the sensors. Figure 8 shows the effect of distributing the load on the surface, wandering, where it is seen sensors. Figure 8 shows the effect of distributing the load on the surface, wandering, where it is seen responses decrease its values as the load moves further from the center, radius 19.0 m. Figure 8 (left), how the responses decrease its values as the load moves further from the center, radius 19.0 m. Figure how the responses decrease its values as the load moves further from the center, radius 19.0 m. Figure PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 (right), 8 (left), PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 8 (left), PFG–H3, shows how the higher responses occur around loading position 6, whereas Figure 8 PFG–H5, shows how the signal disappears with respect to the load positioning. (right), PFG–H5, shows how the signal disappears with respect to the load positioning. (right), PFG–H5, shows how the signal disappears with respect to the load positioning. Figure Figure 8. 8. Evolution Evolution of of voltage voltage for for PFG PFG–H3 –H3 and PFG and PFG–H5, –H5, res respectively pectively. . Figure 8. Evolution of voltage for PFG–H3 and PFG–H5, respectively. Infrastructures 2020, 5, 4 9 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 20 Finally, the most important result from the sensors was the comparison of the evolution of the Finally, the most important result from the sensors was the comparison of the evolution of the Finally, the most important result from the sensors was the comparison of the evolution of the maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses maximum strain/voltage against the number of loads. Figures 9 and 10 show the combined responses from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. from sensor H3 against the T1 and T2 gauges, respectively, with an increasing number of loads/years. The figures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG The ﬁgures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG The figures displayed show graphical plots of sensor voltage (left y-axis) against loading for the PFG sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on sensor and microstrain (right y-axis) against loading for the strain gauges. The plots are not made on the same figure to allow for a combined visualization of the trends over the loading period. Figure 9 the same ﬁgure to allow for a combined visualization of the trends over the loading period. Figure 9 the same figure to allow for a combined visualization of the trends over the loading period. Figure 9 shows an inverse trend around 600,000 load repetitions (12 years), where the responses suffer an shows an inverse trend around 600,000 load repetitions (12 years), where the responses suer an shows an inverse trend around 600,000 load repetitions (12 years), where the responses suffer an important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load important drop/rise from sensors T1 and H3, respectively. A second change occurs at 800,000 load repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other repetitions (16 years), where there is an appearance of cracking at the surface. Figure 10 on the other hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when hand, shows a constant growth for both sensors, which is triggered at 600,000 load repetitions when the responses rapidly increase. the responses rapidly increase. the responses rapidly increase. Figure 9. Sensors T1 and H3 measurements evolution. Figure 9. Sensors T1 and H3 measurements evolution. Figure 9. Sensors T1 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Figure 10. Sensors T2 and H3 measurements evolution. Infrastructures 2020, 5, 4 10 of 20 5. Development of Maintenance Intervention Strategies Given the results from the sensors and gauges, the next step in the study was the development of maintenance intervention strategies based on triggers that could be inferred from the results. To allow for this comparison to be done, a baseline was created of a typical predeﬁned maintenance strategy, over a 30-year period. Environmental impacts imposed during the maintenance of a speciﬁc road stretch could then be compared with the equivalent impacts originating from the maintenance strategy of the same road stretch and period, after utilizing information from the sensors and gauges, optimizing the maintenance strategy. The current approach was setup based on inputs from local experts and previous work in the sector on baseline maintenance strategies [33,34]. This baseline pre-set maintenance plan is given below in Table 2. Table 2. Pre-set maintenance actions for road under analysis. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Baseline 0 0 0 0 0 0 0 0 1,2 0 0 0 0 0 0 4 0 0 0 0 0 0 0 1,2 0 0 0 0 0 0 5 Interventions Where: 0: Do nothing 1: Cracks, rutting, potholes ﬁlling and sealing [Routine Maintenance] 2: Microsurfacing [Routine Maintenance] 3: Thin Overlay—2 cm Hot Mix Asphalt (HMA) [Preventative Maintenance] 4: Conventional structural mill and replace, Wearing course only [Corrective Maintenance] 5: Conventional structural mill and replace, wearing and binder [Corrective Maintenance] The optimized approaches were then generated utilizing information from the gauges as triggers for intervention strategies on the pavement, and are explained in the following section. 5.1. Optimized Plan Based on PFG Sensors Response In order to determine an optimized plan for the PFG sensors, a correct threshold deﬁnition is essential for the cumulative voltage time (CVT) approach. This study used percentiles P-95 and P-05 to deﬁne the upper and lower limits (D7 and D1, respectively) based on the entire responses for each PFG sensor. Once D1 and D7 have been deﬁned, equally space values are calculated (D2 to D6). Table 3 shows the seven thresholds for PFG–H3/H7 and activation number for the “waking-up” of the sensor along with the number of load applications. “Waking-up” of the sensor signiﬁes that it starts recording after a particular number of load applications. Figure 11 (top) shows the CVT for sensor H3, whereas Figure 11 (bottom) shows the CVT for sensor H7. Table 3. Threshold deﬁnition and activations. H3 H7 Threshold Deﬁnition Activation Deﬁnition Activation 1 0.018 – 0.016 – 2 0.025 – 0.028 – 3 0.031 591,200 0.038 125,600 4 0.038 591,200 0.048 125,600 5 0.045 789,200 0.059 N/A 6 0.051 789,200 0.069 N/A 7 0.058 789,200 0.080 N/A Infrastructures 2020, 5, 4 11 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 11 of 20 Figure 11. Cumulative voltage time (CVT) for sensor H3 and H7, respectively. Figure 11. Cumulative voltage time (CVT) for sensor H3 and H7, respectively. Using Figures 9–11, there are speciﬁc points that clearly stand out where the change in response, slope, denotes Using Figur the e appearance s 9–11, there are specific po of damage. Figur ints that e 11 (top) clearly stand o supports the ut where the chan previous statement ge in re wher sponse, e slope, denotes the appearance of damage. Figure 11 (top) supports the previous statement where the the “waking-up” of threshold level 3 and onwards occurs. Points located at 125,000, 591,200, and “waking-up 789,200 load ” of threshold repetitions (3.0, level 12.0, 3 anand d on16.0 wards occ years,u rrs espectively) . Points loca wer ted a e thus t 125selected ,000, 591,200 for developing , and 789,200 load repetitions (3.0, 12.0, and 16.0 years, respectively) were thus selected for developing the the maintenance strategies. ma At intena a loading nce stra value tegies. of 125,000 (equivalent to 3 years of the life cycle), threshold level 2 comes alive. At a loading value of 125,000 (equivalent to 3 years of the life cycle), threshold level 2 comes This can be considered as a point where the post-compaction of the AC layer is concluded and damage starts alive. This c to grow, but an be at consider this poined t, itas is also a point consider where th ed as e po low st-compactio severity distr n of the ess. Given AC layer that the is conc sensors luded have and damage starts to grow, but at this point, it is also considered as low severity distress. Given that the indicated that there is something wrong, the routine maintenance that was expected to be carried out at sensors have year 8 can be moved indicated th up toat there is some now take placething wrong two years after , the routine m the sensors have ainten indicated ance that was expect that something ed to be carried out at year 8 can be moved up to now take place two years after the sensors have indicated is wrong. This intervention is routine maintenance, characterized by minor surface treatments. that The somet nexthinstance ing is wron of noted g. This int change erven bytthe ion is ro sensors utin is e maint at an equivalent enance, char load actof eri591,200 zed by minor (equivalent surface treatments. to 12 years of the life cycle). At this point, the values of threshold levels 1 and 2 have kept growing The next instance of noted change by the sensors is at an equivalent load of 591,200 (equivalent to 12 years of the life cycle). At this point, the values of threshold levels 1 and 2 have kept growing as Infrastructures 2020, 5, 4 12 of 20 as expected. The damage is now considered as medium severity, as threshold levels 1 and 2 have changed their slope and threshold levels 3 and 4 are becoming alive as well. Therefore, given this development, another intervention at year 14 can be planned. At year 13 (two years after the trend has now picked up on the sensor/gauge), intervention 1 and 3 can be introduced instead of waiting for year 15, as was planned in the pre-set maintenance plan. Additionally, intervention 2 can be introduced midway between these planned interventions given the fact that the sensor shows the distress is evolving, and this would enable its development to be reduced, prolonging the necessity of corrective maintenance. At a loading of 789,200 (equivalent to year 16 in the life cycle), the values at threshold levels 1 and 2 grow with no variation in their slope; nonetheless, the values of threshold levels 3 and 4 have increased and threshold levels 5, 6, and 7 have been initiated. This, therefore, infers that the damage is now considered as medium/high severity, indicating that surface cracks are imminent. Therefore, in the optimized plan, interventions can be planned to extend the point at which this will happen. Subsequently, using intervention 3 along with the routine maintenance would be an intervention of less ﬁnancial impact than that of intervention 4, which is scheduled at year 15 in the baseline. It is necessary to note here that, while the sensors in the experimental section only provided data for 20 years, these data do allow for a projection of what can happen over a 30-year period, and thus enable the environmental evaluation over this period. This is based on the common interventions over similar times [33], and thus enable the environmental evaluation over this period. As interventions in years 0–20 have been shifted, the subsequent interventions in the following 10 years can then be shifted as well. Therefore, the next intervention to account for is intervention 4 (shown at year 15 in the baseline). In the optimized approach, intervention 1 can be introduced at year 19 (given the inclusion of a thin overlay at year 13), which accounts for routine surface maintenance. This is in line with the visual inspections in the study, showing cracking appearing at a loading of 910,000 (equivalent to year 18). Consequently, major intervention 4 can be performed at year 23, which would then be at a similar stage to that of an intervention in the baseline given the shift of processes. Finally, at year 30, interventions 1 and 3 can be introduced, which would be at a similar stage to that of an intervention in the baseline, but much less signiﬁcant than the full intervention 5. This, therefore, indicates that, within the optimized 30-year period under analysis, intervention 5 is not carried out. It must be noted that, over the full life cycle of the pavement, this intervention would be needed. However, by lengthening its life expectancy carrying out more preventative and correction intervention actions, intervention 5 has been shifted out of the assessment period. This means that the impacts, both ﬁnancially and environmentally, would be spread over a longer period. Considering the loading values and the inferences made from the gate activity, the optimized maintenance plan is given in Table 4. Table 4. Optimized maintenance action plans based on responses from piezo ﬂoating gate (PFG)-H3 sensors. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Optimized 0 0 0 0 0 1 0 0 0 2 0 0 0 1,3 0 0 0 0 0 1 0 0 0 4 0 0 0 0 0 0 1,3 Interventions 5.2. Optimized Plan Based on Asphalt Strain Gauges Response For the strain gauges utilized in the test section, similar trigger points were observed (Figures 9 and 10). Considering the trend shown by sensor T1 (Figure 9), the ﬁrst point to be considered as a trigger for intervention is at 207,600 loads (equivalent to four years). This point is based on the standard deviation of the max values shown from the sensor, which indicate that there is some damage is occurring within the pavement. The routine maintenance that was expected to be carried out at year 8 can be moved up to two years after the sensors have indicated that something is wrong, similar to the approach taken with the results from the PFG sensors. This is a year after the similar intervention is done with sensor H3. The next instance of noted change by the gauges is at an equivalent load of Infrastructures 2020, 5, 4 13 of 20 591,200 (equivalent to 12 years of the life cycle), which again is similar to the results of sensor H3. However, given that the ﬁrst noted change from the gauges is a year later than the PFG sensors, it can reasonably be assumed that the damage would have worsened further without any action, as opposed to the road monitoring scenario with the PFG sensor. This assumption is appropriate given the fact that it is the same pavement under analysis. Given this, an additional routine maintenance was inserted to ensure the condition is kept optimal. Therefore, routine maintenance intervention 1 was inserted at year 11 along with intervention 2 at year 10. Intervention 2 can be introduced midway between these planned interventions given the fact that the gauge is showing the distress is evolving, and this would enable its development to be reduced, prolonging the necessity of corrective maintenance. Subsequently, at year 15, intervention 1 and 3 can be introduced at the same point that intervention 4 was planned in the pre-set maintenance plan. A similar approach, as applied with the plan for the PFG sensors, for the interventions taking place between years 20–30 was also applied for the strain gauge. Therefore, the next intervention to account for is intervention 4 (shown at year 15 in the baseline). In the optimized approach, intervention 1 can be introduced at year 20 (given the inclusion of a thin overlay at year 15), which accounts for routine surface maintenance. This is also in line with the results from the PFG sensors indicating that, at a loading of 900,000 (equivalent to year 18), there are cracks appearing at the surface. Then, subsequent to this, major intervention 4 can be performed at year 24, which would then be at a similar stage to that of an intervention in the baseline given the shift of processes. Finally, at year 30, interventions 1 and 3 can be introduced, which would be at a similar stage to that of an intervention in the baseline, but much less than the full intervention 5. Considering the loading values and the inferences made from the threshold activity, the optimized maintenance plan is given in Table 5. Table 5. Optimized maintenance action plans based on responses from strain gauges. Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Optimized 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 1,3 0 0 0 0 1 0 0 0 4 0 0 0 0 0 1,3 Interventions Comparable to the optimized plan for the sensors, intervention 5 is not carried out. Additionally, as previously indicated, within the full life cycle of pavement (which would now be more than the 30 years under analysis), this intervention would be needed. Similar to the plan for sensor H3, the impacts, both ﬁnancially and environmentally, would be allocated over a longer period, making the eect less per year. 6. The Use of Life Cycle Assessment Having identiﬁed dierent maintenance pipelines for each scenario, it was noted that, using the monitoring techniques, there are now more interventions occurring. They are of a lesser value in terms of work and ﬁnancial costs, but together they signify a larger number of interventions than in the pre-planned system. Therefore, the question arises as to whether using more interventions will cause more damage to the environment, even if they are extending the life of the pavement. Given the knowledge that the maintenance and rehabilitation of asphalt pavements can be quite environmentally impactful [35], the next step was to quantify their environmental impacts in terms of LCA indicators. 6.1. Goal and Scope Deﬁnition The study was performed in order for the environmental impacts of the three alternative product systems to be quantiﬁed and compared. The product systems include the maintenance phase of the same stretch of asphalt pavement. In the baseline scenario, the maintenance regime to be followed is one typical of usually implemented typical predeﬁned maintenance strategies. The two alternatives consist of optimized maintenance planning based upon the data acquired from the dierent types of sensing technologies that have been embedded in the corresponding pavement structures. The study Infrastructures 2020, 5, 4 14 of 20 thus aims to quantify the environmental burdens aliated with the maintenance pipeline of the two Infrastructures 2020, 5, x FOR PEER REVIEW 14 of 20 optimized scenarios and identify whether they exhibit an improvement compared with the baseline. 6.2. Functional Unit 6.2. Functional Unit The functional unit that was utilized for the specific study can be described as 1 m of the asphalt The functional unit that was utilized for the speciﬁc study can be described as 1 m of the asphalt pavement’s surface, along with the underlying asphaltic layers, providing adequate performance for pavement’s surface, along with the underlying asphaltic layers, providing adequate performance for a a period of 30 years. In detail, two bituminous layers are technically included and are both composed period of 30 years. In detail, two bituminous layers are technically included and are both composed by by the EME2 asphalt mixture, a high modulus mixture widely used in road engineering in France the EME2 asphalt mixture, a high modulus mixture widely used in road engineering in France [36]. [36]. The functional unit can be seen in Figure 12. The functional unit can be seen in Figure 12. Figure 12. Functional unit of the life cycle assessment (LCA) exercises. Figure 12. Functional unit of the life cycle assessment (LCA) exercises. 6.3. System Boundaries 6.3. System Boundaries Having deﬁned the functional unit, the next step was the deﬁnition of the system boundaries. Having defined the functional unit, the next step was the definition of the system boundaries. As deﬁned in dierent guidance documents [22,23,37], the four stages of an asphalt pavement’s life As defined in different guidance documents [22,23,37], the four stages of an asphalt pavement’s life cycle are the product stage, the construction stage, the use stage, and the end of life stage. In this study, cycle are the product stage, the construction stage, the use stage, and the end of life stage. In this the selected system boundaries are limited to the use stage and speciﬁcally to B2: maintenance, B3: study, the selected system boundaries are limited to the use stage and specifically to B2: maintenance, repair, and B4: replacement. The reason behind this methodological choice is the fact that the LCA B3: repair, and B4: replacement. The reason behind this methodological choice is the fact that the LCA exercises are conducted for the same pavement structure and the same analysis period of 30 years. exercises are conducted for the same pavement structure and the same analysis period of 30 years. Thus, the environmental impacts arising from the product, the construction, and the end of life stages Thus, the environmental impacts arising from the product, the construction, and the end of life stages would be the same. However, having optimized maintenance pipelines per alternative also indicates would be the same. However, having optimized maintenance pipelines per alternative also indicates that there are potential dierences in their environmental severity. The system boundaries for all three that there are potential differences in their environmental severity. The system boundaries for all alternatives can be seen in Figure 13. However, it is worth mentioning that, in the use phase (i.e., three alternatives can be seen in Figure 13. However, it is worth mentioning that, in the use phase maintenance, repair, replacement), the processes related to the raw materials needed to be extracted (i.e., maintenance, repair, replacement), the processes related to the raw materials needed to be in order for the maintenance to be achieved; their transport to the mixing plant and the construction extracted in order for the maintenance to be achieved; their transport to the mixing plant and the site, along with the production of products necessary for the maintenance of the pavements, were construction site, along with the production of products necessary for the maintenance of the included in the system boundaries. It should also be noted that more frequent interventions in the pavements, were included in the system boundaries. It should also be noted that more frequent road segment, even if of lower signiﬁcance/duration), could potentially impose higher costs for the interventions in the road segment, even if of lower significance/duration), could potentially impose users. However, as the analysis conducted in this study focuses only on the environmental pillar higher costs for the users. However, as the analysis conducted in this study focuses only on the of sustainability, the user/agency costs were not taken into consideration. Moreover, the required environmental pillar of sustainability, the user/agency costs were not taken into consideration. interventions now, in the optimized scenario, consist of mainly surface treatments and not structural Moreover, the required interventions now, in the optimized scenario, consist of mainly surface interventions. That would mean that, indeed, a higher amount of interventions would occur, but at the treatments and not structural interventions. That would mean that, indeed, a higher amount of same time, these interventions would cost, in terms of time, signiﬁcantly less. Finally, the environmental interventions would occur, but at the same time, these interventions would cost, in terms of time, impacts of these interventions have already been quantiﬁed, utilizing the maintenance itself as system significantly less. Finally, the environmental impacts of these interventions have already been boundaries. Environmental impacts related to trac congestion or rerouting were not taken into quantified, utilizing the maintenance itself as system boundaries. Environmental impacts related to consideration, based on the very deﬁnition of our product system. traffic congestion or rerouting were not taken into consideration, based on the very definition of our For the completion of the LCA case studies, Gabi ts, by Thinkstep [38], was utilized along product system. with the Gabi Professional and Ecoinvent 3 databases. Moreover, no primary data were acquired, For the completion of the LCA case studies, Gabi ts, by Thinkstep [38], was utilized along with and instead reputable data sources and literature, reports, international standards, product category the Gabi Professional and Ecoinvent 3 databases. Moreover, no primary data were acquired, and rules (PCRs), and environmental product declarations (EPDs) were used [22,23,37,39–43]. In terms instead reputable data sources and literature, reports, international standards, product category rules (PCRs), and environmental product declarations (EPDs) were used [22,23,37,39–43]. In terms of impact assessment methodology, ReCiPe 2016, Hierarchist (H) [44] was utilized for both Mid and Endpoint indicators. Infrastructures 2020, 5, 4 15 of 20 of impact assessment methodology, ReCiPe 2016, Hierarchist (H) [44] was utilized for both Mid and Endpoint indicators. Infrastructures 2020, 5, x FOR PEER REVIEW 15 of 20 Figure 13. Figure 13. Sy System stem bou boundaries ndaries of the of the produ product ct sy system. stem. 6.4. Life Cycle Assessment Results 6.4. Life Cycle Assessment Results As mentioned before, ReCiPe 2016 (H) was utilized as the impact assessment methodology in this As mentioned before, ReCiPe 2016 (H) was utilized as the impact assessment methodology in study. Thus, as proposed within the very same methodology and for reasons concerning the increased this study. Thus, as proposed within the very same methodology and for reasons concerning the accuracy of the study, the analysis of the LCA impact category indicators was conducted in two levels: increased accuracy of the study, the analysis of the LCA impact category indicators was conducted MidPoint and EndPoint. The midpoint indicators are associated with a wider range of analysis, while in two levels: MidPoint and EndPoint. The midpoint indicators are associated with a wider range of the endpoint impact category indicators correspond to three aggregated indicators, also characterized analysis, while the endpoint impact category indicators correspond to three aggregated indicators, as areas of protection (AOP). It is recommended for the two levels of analyses to be implemented in also characterized as areas of protection (AOP). It is recommended for the two levels of analyses to parallel, in order for more informed results to be acquired [44]. be implemented in parallel, in order for more informed results to be acquired [44]. 6.4.1. MidPoint Impact Category Indicators 6.4.1. MidPoint Impact Category Indicators Following the life cycle impact assessment and life cycle interpretation phases that were undertaken Following the life cycle impact assessment and life cycle interpretation phases that were for the realization of this study, the MidPoint impact category indicators can be found in Figure 14 for undertaken for the realization of this study, the MidPoint impact category indicators can be found in all three scenarios. This radar graph indicates the relative percentage variation of the values of the Figure 14 for all three scenarios. This radar graph indicates the relative percentage variation of the MidPoint impact category indicators compared with the baseline. The latter is depicted as the black values of the MidPoint impact category indicators compared with the baseline. The latter is depicted dashed line, while in red and green colors, the scenarios with the strain gauges and the PFG sensors as the black dashed line, while in red and green colors, the scenarios with the strain gauges and the can be found, respectively. PFG sensors can be found, respectively. From Figure 14, it can be seen that, in both the scenarios, where embedded sensing technologies From Figure 14, it can be seen that, in both the scenarios, where embedded sensing technologies were utilized, all the MidPoint impact category indicators exhibit lower values compared with the were utilized, all the MidPoint impact category indicators exhibit lower values compared with the baseline. This can be explained by the fact that, when the aforementioned sensing technologies are baseline. This can be explained by the fact that, when the aforementioned sensing technologies are utilized, the amount of available data about the condition of the pavement is signiﬁcantly higher utilized, the amount of available data about the condition of the pavement is significantly higher compared with situations where no sensors are utilized. Thus, more informed decisions regarding the compared with situations where no sensors are utilized. Thus, more informed decisions regarding way of selecting the most appropriate maintenance strategy and the most beneﬁcial time interval to the way of selecting the most appropriate maintenance strategy and the most beneficial time interval execute it can be achieved. Moreover, it can also be said that, when PFG sensors are used, the values of to execute it can be achieved. Moreover, it can also be said that, when PFG sensors are used, the the MidPoint indicators are slightly lower compared with the two other scenarios, because, with this values of the MidPoint indicators are slightly lower compared with the two other scenarios, because, with this technology, it became possible to acquire “warning data” concerning the condition of the pavement earlier that when conventional strain gauges were utilized. Infrastructures 2020, 5, 4 16 of 20 technology, it became possible to acquire “warning data” concerning the condition of the pavement earlier that when conventional strain gauges were utilized. Infrastructures 2020, 5, x FOR PEER REVIEW 16 of 20 Figure 14. Relative percentage variation of the MidPoint indicators’ values. Figure 14. Relative percentage variation of the MidPoint indicators’ values. 6.4.2. EndPoint Impact Category Indicators 6.4.2. EndPoint Impact Category Indicators The next step to be undertaken according to the ReCiPe 2016 (H) impact assessment The next step to be undertaken according to the ReCiPe 2016 (H) impact assessment methodology methodology is the quantification of the EndPoint impact category indicators. They are able to is the quantiﬁcation of the EndPoint impact category indicators. They are able to provide a broader provide a broader view of the impacts that the studied scenarios impose in the environment. In detail, view of the impacts that the studied scenarios impose in the environment. In detail, they allocate the they allocate the damages originating from the maintenance regimes of the three scenarios in three damages originating from the maintenance regimes of the three scenarios in three areas of protection; areas of protection; namely, damage to human health measured in disability-adjusted life years namely, damage to human health measured in disability-adjusted life years (DALY), damage to (DALY), damage to ecosystems expressed as time-integrated species loss, and damage to resource ecosystems expressed as time-integrated species loss, and damage to resource availability with surplus availability with surplus cost as its metric. In Figure 15, the values of the EndPoint impact category cost as its metric. In Figure 15, the values of the EndPoint impact category indicators per scenario indicators per scenario are displayed. are displayed. As Figure 15 depicts, the values of the Endpoint indicators are slightly reduced for both scenarios As Figure 15 depicts, the values of the Endpoint indicators are slightly reduced for both scenarios that utilize embedded sensing technologies. In detail, the values of the EndPoint indicators for the that utilize embedded sensing technologies. In detail, the values of the EndPoint indicators for the scenario with the PFG sensors are slightly reduced compared with those corresponding to the scenario with the PFG sensors are slightly reduced compared with those corresponding to the scenario scenario where conventional strain gauges were used. where conventional strain gauges were used. Infrastructures 2020, 5, 4 17 of 20 Infrastructures 2020, 5, x FOR PEER REVIEW 17 of 20 Figure 15. Values of the EndPoint impact category indicators per scenario. Figure 15. Values of the EndPoint impact category indicators per scenario. 7. Summary and Conclusions 7. Summary and Conclusions In this study, two embedded sensing technologies (PFG sensors and conventional asphalt In this study, two embedded sensing technologies (PFG sensors and conventional asphalt strain strain gauges) were used to develop optimized maintenance plans for road pavement management. gauges) were used to develop optimized maintenance plans for road pavement management. In In addition, in order to assess the environmental impact of the optimized approaches, a life cycle addition, in order to assess the environmental impact of the optimized approaches, a life cycle assessment (LCA) exercise was carried out. assessment (LCA) exercise was carried out. The sensors were located at the bottom of the asphalt layer of a section of the APT facility owned The sensors were located at the bottom of the asphalt layer of a section of the APT facility owned and managed by the IFSTTAR. Their responses were collected over the duration of the experiment and managed by the IFSTTAR. Their responses were collected over the duration of the experiment (three months) and analyzed in terms of maximum strain/voltage evolution with the increasing number (three months) and analyzed in terms of maximum strain/voltage evolution with the increasing of loads. The results show that both technologies depict the weakening of the pavement structure with number of loads. The results show that both technologies depict the weakening of the pavement strain and voltage measurements having a good correlation for the majority of the experiment. It was, structure with strain and voltage measurements having a good correlation for the majority of the however, noted that there were signiﬁcant dierences in the maximum microstrain measurements experiment. It was, however, noted that there were significant differences in the maximum from the strain gauges for the T1 and T2 gauges. This is important as it demonstrates that, for this microstrain measurements from the strain gauges for the T1 and T2 gauges. This is important as it technology, it is critical to have an array of devices employed to allow for critical evaluation and demonstrates that, for this technology, it is critical to have an array of devices employed to allow for accuracy by pavement engineers. This issue was not detected with the results of the PFG sensors, critical evaluation and accuracy by pavement engineers. This issue was not detected with the results as there was more consistency in the results of these sensors, and thus this represents an advantage of of the PFG sensors, as there was more consistency in the results of these sensors, and thus this utilizing this technology. Additionally, the newer version of piezoelectric sensors is wireless, which represents an advantage of utilizing this technology. Additionally, the newer version of piezoelectric decreases ﬁeld installation complexity and creates more ﬂexible data transmission protocols that can sensors is wireless, which decreases field installation complexity and creates more flexible data be associated with the strain gauges. Furthermore, piezoelectric sensors will be categorized as low-cost transmission protocols that can be associated with the strain gauges. Furthermore, piezoelectric solutions, which makes them more attractive not only for research projects, but for real case studies. sensors will be categorized as low-cost solutions, which makes them more attractive not only for Sensors T2 and H3 (Figure 10) suer a drastic change in slope at approximately 600,000 load research projects, but for real case studies. repetitions. After this point, deﬁned as critical, damage in the AC layer will grow at a much higher Sensors T2 and H3 (Figure 10) suffer a drastic change in slope at approximately 600,000 load rate, which may lead to structural failure if no maintenance is scheduled. The results from sensor repetitions. After this point, defined as critical, damage in the AC layer will grow at a much higher H3, Figure 11 (top), support this as the middle thresholds (3 and 4, Table 3) also become alive at rate, which may lead to structural failure if no maintenance is scheduled. The results from sensor H3, approximately 600,000 load repetitions. Finally, surface cracking is seen around 900,000 load repetitions, Figure 11 (top), support this as the middle thresholds (3 and 4, Table 3) also become alive at when all the thresholds have been wakened up, which validates the idea of using PFG sensors for approximately 600,000 load repetitions. Finally, surface cracking is seen around 900,000 load preventive pavement monitoring. repetitions, when all the thresholds have been wakened up, which validates the idea of using PFG sensors for preventive pavement monitoring. Infrastructures 2020, 5, 4 18 of 20 While the sensor results allowed for the generation of optimized pavement maintenance strategies, the LCA case study allowed for an evaluation of the environmental friendliness of these actions. The evaluation was done utilizing a commercial software, Gabi ts, and for life cycle impact assessment methodology, ReCiPe 2016 (H) was selected. Through this assessment, it was shown that both strategies presented environmental beneﬁts during the maintenance phase of the asphalt pavements, with the PFG sensors providing the greater beneﬁts of the two. It can thus be deduced that, when sensing technologies are embedded in the pavement structure and are able to provide the decision-makers with “in-time” information about the condition of the pavement, more informed choices can be made that lead to more environmentally friendly maintenance regimes, compared with the case when sensors are absent. A critical point in the analysis is that the optimized schedules allow for an extended life cycle of the pavement. This is done by carrying out more preventative maintenance interventions and not waiting on the more environmentally harmful corrective maintenance interventions when pavement failure has already occurred. These results present a pipeline for practical application of utilizing the embedded technologies within the pavement management industry. The use of the sensors has practical advantages given their embedded nature and, therefore, the lack of intrusiveness provided by the data collected from these sensors as compared with other technologies that require consistent physical intervening surveys. However, there is still more research to be done on these technologies given doubts with regards to the technologies’ life expectancy, the reliability of the results, and their end of life strategies. The failure of sensors needs to be further examined in future work as well, because once a sensor has suered failure, there is no easy solution given that they are embedded. Given these drawbacks to the technologies, this study helps to bridge these gaps by providing an understanding how to utilize the data from these sensors and how they can be interpreted for maintenance strategy formulation. It should also be noted that the cost of implementation of the sensors needs to be fully understood in future works, as this is a key component to practical implementation. More real-world experiments, similar to those in the study, will help validate and support the use of these technologies moving forward. Future developments should line up with the miniaturization of the technology, reducing complexity for ﬁeld deployments, and providing easy ways for visualization and management of the data. Author Contributions: Conceptualization, M.M.-P., R.R., M.B., and K.M.; methodology, M.M.-P., R.R., M.B., and K.M.; software, M.M.-P., R.R., and K.M.; validation, M.M.-P., R.R., M.B., and K.M.; formal analysis, M.M.-P., R.R., and K.M.; investigation, M.M.-P., R.R., M.B., and K.M.; resources, M.M.-P., R.R., M.B., and K.M.; data curation, M.M.-P., R.R., M.B., and K.M.; writing—original draft preparation, M.M.-P., R.R., and K.M.; writing—review and editing, M.M.-P., R.R., M.B., and K.M.; visualization, M.M.-P., R.R., and K.M. All authors have read and agreed to the published version of the manuscript. Funding: The research presented in this paper was carried out as part of the H2020-MSCA-ETN-2016. This project has received funding from the European Union’s H2020 Programme for research, technological development, and demonstration under grant agreement number 721493. Acknowledgments: The Accelerated Pavement Testing was carried out within the BioRePavation project. It was co-funded by Funding Partners of the ERA-NET plus Infravation and the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement number 607524. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. Vandam, T.J.; Harvey, J.T.; Muench, S.T.; Smith, K.D.; Snyder, M.B.; Al-Qadi, I.L.; Ozer, H.; Meijer, J.; Ram, P.V.; Roesier, J.R.; et al. Towards Sustainable Pavement Systems: A Reference Document FHWA-HIF-15-002; Federal Highway Administration: Washington, DC, USA, 2015. 2. International Road Federation (IRF). IRF World Road Statistics 2018 (Data 2011–2016); International Road Federation (IRF): Brussels, Belgium, 2018. 3. Peterson, D. National Cooperative Highway Research Program Synthesis of Highway Practice Pavement Management Practices. No. 135; Transportation Research Board: Washington, DC, USA, 1987; ISBN 0309044197. Infrastructures 2020, 5, 4 19 of 20 4. Li, X.; Goldberg, D.W. Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urban Syst. 2018, 69, 51–62. [CrossRef] 5. Radopoulou, S.C.; Brilakis, I. Improving Road Asset Condition Monitoring. Transp. Res. Procedia 2016, 14, 3004–3012. [CrossRef] 6. Ragnoli, A.; De Blasiis, M.; Di Benedetto, A. Pavement Distress Detection Methods: A Review. Infrastructures 2018, 3, 58. [CrossRef] 7. Coenen, T.B.J.; Golroo, A. A review on automated pavement distress detection methods. Cogent Eng. 2017, 4, 1374822. [CrossRef] 8. Inzerillo, L.; Di Mino, G.; Roberts, R. Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Autom. Constr. 2018, 96, 457–469. [CrossRef] 9. Xue, W.; Wang, D.; Wang, L. A review and perspective about pavement monitoring. Int. J. Pavement Res. Technol. 2012, 5, 295–302. 10. Lajnef, N.; Rhimi, M.; Chatti, K.; Mhamdi, L.; Faridazar, F. Toward an integrated smart sensing system and data interpretation techniques for pavement fatigue monitoring. Comput. Civ. Infrastruct. Eng. 2011, 26, 513–523. [CrossRef] 11. Merenda, M.; Praticò, F.G.; Fedele, R.; Carotenuto, R.; Corte, F.G. Della A real-time decision platform for the management of structures and infrastructures. Electronics 2019, 8, 1180. [CrossRef] 12. Fedele, R.; Praticò, F.G.; Carotenuto, R.; Della Corte, F.G. Energy savings in transportation: Setting up an innovative SHM method. Math. Model. Eng. Probl. 2018, 5, 323–330. [CrossRef] 13. Hasni, H.; Alavi, A.H.; Chatti, K.; Lajnef, N. A self-powered surface sensing approach for detection of bottom-up cracking in asphalt concrete pavements: Theoretical/numerical modeling. Constr. Build. Mater. 2017, 144, 728–746. [CrossRef] 14. American Association of State Highway and Transportation Ocials (AASHTO). Pavement Management Guide; American Association of State Highway and Transportation Ocials (AASHTO): Washington, DC, USA, 2012. 15. Xue, W.; Wang, L.; Wang, D.; Druta, C. Pavement Health Monitoring System Based on an Embedded Sensing Network. J. Mater. Civ. Eng. 2014, 26, 04014072. [CrossRef] 16. Duong, N.S.; Blanc, J.; Hornych, P.; Bouveret, B.; Carroget, J.; Le feuvre, Y. Continuous strain monitoring of an instrumented pavement section. Int. J. Pavement Eng. 2018, 8436, 1435–1450. [CrossRef] 17. Gaborit, P.; Sauzéat, C.; Di Benedetto, H.; Pouget, S.; Olard, F.; Claude, A.; Monnet, A.J.; Audin, R.M.; Sauzéat, C.; Di Benedetto, H.; et al. Investigation of highway pavements using in-situ strain sensors. Int. Conf. Transp. Infrastruct. 2013, 28, 331. 18. Blanc, J.; Hornych, P.; Duong, N.S.; Blanchard, J.Y.; Nicollet, P. Monitoring of an experimental motorway section. Road Mater. Pavement Des. 2017, 20, 74–89. [CrossRef] 19. Stripple, H. Life Cycle Assessment of Road, A Pilot Study for Inventory Analysis; IVL RAPPORT. 1210; IVL: Linköping, Sweden, 2001; Volume 2. 20. Carlson, A. Life Cycle Assessment of Roads and Pavements—Studies Made in Europe; VTI Rapp. 736A; Statens väg-och transportforskningsinstitut: Linköping, Sweden, 2011; Volume 22. 21. ECRPD. Energy Conservation in Road Pavement Design, Maintenance and Utilisation. 2010, pp. 1–63. Available online: https://ec.europa.eu/energy/intelligent/projects/sites/iee-projects/ﬁles/projects/documents/ ecrpd_publishable_report_en.pdf (accessed on 29 December 2019). 22. The International Standards Organisation. Environmental Management—Life Cycle Assessment—Requirements and Guidelines; ISO 14044; The International Standards Organisation: Geneva, Switzerland, 2006; pp. 652–668. 23. International Organization for Standardization. ISO 14040-Environmental Management—Life Cycle Assessment—Principles and Framework; The International Standards Organisation: Geneva, Switzerland, 2006; Volume 3, p. 20. 24. Chappat, M.; Bilal, J. La route écologique du futur—Analyse du Cycle de Vie, Consommation d’énergie & émission de gaz à eet de serre. Développement Durable 2003, 40. 25. Häkkinen, T.; Mäkelä, K. Environmental Impact of Concrete and Asphalt Pavements; Technical Research Center of Finland: Espoo, Finland, 1996; ISBN 9513849074. 26. Mroueh, U.M.; Laine-Ylijoki, J.; Eskola, P. Life-cycle impacts of the use of industrial by-products in road and earth construction. Waste Manag. Ser. 2000, 1, 438–448. Infrastructures 2020, 5, 4 20 of 20 27. Hoang, T.; Jullien, A.; Ventura, A.; Crozet, Y. A global methodology for sustainable road—Application to the environmental assessment of French highway. In Proceedings of the 10 DBMC International Conference On Durability of Building Materials and Components, Lyon, France, 17–20 April 2005. 28. Tokyo Measuring Instruments Lab. TML Pam E-2020C Tranducers 2017. Available online: https://tml.jp/eng/ documents/Catalog/Transducers_E2020C.pdf (accessed on 12 November 2019). 29. Karim, C.; Alavi, A..; Hassene, H.; Lajnef, N.; Faridazar, F. Damage Detection in Pavement Structures Using Self-powered Sensors. In Proceedings of the 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements, Nantes, France, 6–9 June 2016; Springer: Berlin, Germany, 2016; pp. 665–671. 30. Lajnef, N.; Chatti, K.; Chakrabartty, S.; Rhimi, M.; Sarkar, P. Smart Pavement Monitoring System; Federal Highway Administration: Washington, DC, USA, 2013. 31. Alavi, A.H.; Hasni, H.; Lajnef, N.; Chatti, K.; Faridazar, F. An intelligent structural damage detection approach based on self-powered wireless sensor data. Autom. Constr. 2016, 62, 24–44. [CrossRef] 32. Loboratoire Central des Ponts et Chaussées. French Design Manual for Pavement Structures; Loboratoire Central des Ponts et Chaussées: Paris, France, 1997. 33. SETRA-LCPC. Catalogue des Structures Types de chaussées Neuves; SETRA-LCPC: Paris, France, 1998. 34. Laurent, G. Evaluation économique des chaussées en béton et classiques sur le réseau routier national français; ETUDES ET RECHERCHES DES LABORATOIRES DES PONTS ET CHAUSSEES-SERIE: ROUTES CR 33; ETUDES ET RECHERCHES DES LABORATOIRES DES PONTS ET CHAUSSEES: Paris, France, 2004. 35. Mantalovas, K.; Di Mino, G. The sustainability of reclaimed asphalt as a resource for road pavement management through a circular economic model. Sustainbility 2019, 11, 2234. [CrossRef] 36. Petho, L.; Denneman, E. Implementing EME2, the French high modulus asphalt in Australia. In Proceedings of the 6th Eurasphalt & Eurobitume Congress, Prague, Czech Republic, 1–3 June 2016. 37. EAPA. Guidance Document for Preparing Product Category Rules (Pcr) and Environmental Product Declarations (Epd) for Asphalt Mixtures; EAPA: Arlington County, VA, USA, 2017; p. 43. 38. Thinkstep AG Gabi ts 2019. Available online: https://www.thinkstep.com/content/gabi-databases-2019- edition (accessed on 29 December 2019). 39. The Norwegian EPD Foundation. Product-Category Rules (PCR) for Preparing an Environmental Product Declaration (EPD) for Product Group Asphalt and Crushed Stone; The Norwegian EPD Foundation: Skien, Norway, 2009. 40. EAPA. The Use of Warm Mix Asphalt; EAPA—Position paper; EAPA: Arlington County, VA, USA, 2014; p. 23. 41. BRE. Product Category Rules for Type III Environmental Product Declaration of Construction Products to EN 15804:2012; BRE Group: Watford, UK, 2013; p. 43. 42. European Bitumen Association. Life Cycle Inventory: Bitumen; European Bitumen Association: Harrogate, UK, 2012; ISBN 2930160268. 43. EC-European Commission. COMMISSION RECOMMENDATION of 9 April 2013 on the Use of Common Methods to Measure and Communicate the Life Cycle Environmental Performance; EC-European Commission: Brussels, Belgium, 2013; pp. 1–45. 44. Huijbregts, M.A.J.; Steinmann, Z.J.N.; Elshout, P.M.F.; Stam, G.; Verones, F.; Vieira, M.D.M.; Zijp, M.; van Zelm, R. ReCiPe 2016: A harmonized life cycle impact assessment method at midpoint and enpoint level—Report 1: Characterization. Natl. Inst. Public Health Environ. 2017, 22, 138–147. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

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Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

Towards More Sustainable Pavement Management Practices Using Embedded Sensor Technologies

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