Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

Mosbeh R. Kaloop; Jong Wan Hu; Emad Elbeltagi

doi:10.3390/app6010024

Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

Kaloop, Mosbeh R.;Hu, Jong Wan;Elbeltagi, Emad 2016-01-18 00:00:00 applied sciences Article Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System 1 , 2 1 , 3 , 4 Mosbeh R. Kaloop , Jong Wan Hu * and Emad Elbeltagi Received: 14 October 2015; Accepted: 11 January 2016; Published: 18 January 2016 Academic Editor: César M. A. Vasques Department of Civil and Environmental Engineering, Incheon National University, Incheon 406-840, Korea; mosbeh.kaloop@gmail.com Public Works and Civil Engineering Department, Mansoura University, Mansoura 35516, Egypt Incheon Disaster Prevention Research Center, Incheon National University, Incheon 406-840, Korea Structural Engineering Department, Mansoura University, Mansoura 35516, Egypt; eelbelta@mans.edu.eg * Correspondence: jongp24@incheon.ac.kr; Tel.: +0082-32-835-8463 Abstract: Recently, trains’ velocities in Korea increased more than the speed used in the design of some bridges. Accordingly, this paper demonstrates the evaluation of a railway bridge due to high-speed trains’ movement. A nondestructive monitoring system is used to assess the bridge performance under train speeds of 290, 360, 400 and 406 km/h. This system is comprised of a wireless short-term acceleration system and strain monitoring sensors attached to the bridge girder. The results of the analytical methods in time and frequency domains are presented. The following conclusions are obtained: the cross-correlation models for accelerations and strain measurements are effective to predict the performance of the bridge; the static behavior is increased with train speed developments; and the vibration, torsion, fatigue and frequency contents analyses of the bridge show that the bridge is safe under applied trains’ speeds. Keywords: high-speed; bridge; strain; monitoring; prediction 1. Introduction Nowadays, transportation by high-speed trains is considered as one of the important transportation facilities in South Korea as well as in the world’s high income countries.Therefore, increasing of the trains’ speed is one of the main problems facing existing infrastructure facilities. In Korea, high speed trains startedin 1992 from Seoul to Busan, while the Korea Train eXpress (KTX) services were launched on 1 April 2004.The bridges and infrastructure of express trains have been developing continuously from 1970. Most high-speed railway bridges are designed based on 350 km/h velocity. Therefore,with the velocity increase, existing bridges should be redesigned and evaluated. The newly-completed train HEMU-430X is currently running at high speed over 400km/h in the transportation network of Korea. Therefore, this study aims to evaluate the existing composite steel Kaya Bridge of Seoul-Busan High-Speed Railway under the effect of the high speed train movement. The acceleration and strain measurements are used to evaluate the composite bridge under velocities between 290 to 406 km/h. Lee et al. [1] evaluated steel and pre-stressed concrete (PC) box girder bridgesunder high speed trains up to 289.3 km/h. From their study, they found that no noticeable differences of dynamic responses due to the different materials (steel or concrete) could be found. Xia et al. [2] evaluated the real observation for the multi-span PC of high-speed railway bridges in a time domain. In their study, they recommended the use of the results as a reference for the design of high-speed railway bridges. Ding et al. [3] used the long term acceleration measurements to evaluate high-speed railway steel bridges. More monitoring systems for the effect of high-speed railway trains on bridges can Appl. Sci. 2016, 6, 24; doi:10.3390/app6010024 www.mdpi.com/journal/applsci Appl. Sci. 2016, 6, 24 2 of 13 be found in [4–7]. In general, the main objective of the structural health monitoring (SHM) systems is collecting the observations or information to detect and assess bridge condition, damage, fatigue Appl. Sci. 2016, 6, 24 2 of 13 and performance for proper and timely maintenance intervention. In order to identify the modes of bridge characteristics, it is necessary to excite the structure in order to produce a response at each in [4–7]. In general, the main objective of the structural health monitoring (SHM) systems is collecting relevant mode. The loads and response of structures are parameters for the monitoring of the bridge the observations or information to detect and assess bridge condition, damage, fatigue and performance performanc under e for proper an current and d timely m future loadings aintenance conditions. intervention. In order to Typical SHM idimplementations entify the modes ofin bridge highway characteristics, it is necessary to excite the structure in order to produce a response at each relevant and steel bridges are summarized in [8,9]. In addition, for continuous health monitoring studies, mode. The loads and response of structures are parameters for the monitoring of the bridge the response monitoring technique is more suitable [10]. performance under current and future loadings conditions. Typical SHM implementations in The evaluation methodologies of high speed railway bridges are concluded in [11–13]. highway and steel bridges are summarized in [8,9]. In addition, for continuous health monitoring Sartos et al. [14] assessed the stress/strain levels, load distributions, and fatigue for four different studies, the response monitoring technique is more suitable [10]. bridges based on strain measurements, and they concluded that the system is effective in the The evaluation methodologies of high speed railway bridges are concluded in [11–13]. static performance analysis. Xia et al. [15] used asimulation model to evaluate vertical and lateral Sartos et al. [14] assessed the stress/strain levels, load distributions, and fatigue for four different bridge behavior under high-speed trains. The results of their study showed that the deﬂections and bridges based on strain measurements, and they concluded that the system is effective in the static accelerations of the bridge girder are in accordance with the safety and comfort standards of bridges and performance analysis. Xia et al. [15] used asimulation model to evaluate vertical and lateral bridge running beha train vior under high-speed trains vehicles. Ding et al. [3] pr . Th oposed e results of the parametric their study (polynomial showed that ﬁtting) the deflectio and nonparametric ns and accelerations of the bridge girder are in accordance with the safety and comfort standards of bridges (correlation models, mean value control, root mean square (RMS)) statistical methodology for the and running train vehicles. Ding et al. [3] proposed the parametric (polynomial fitting) and acceleration measurements to study the safety and early-warning of the bridge. From their study, nonparametric (correlation models, mean value control, root mean square (RMS)) statistical they found that the quadratic polynomial ﬁtting provides a good capability for detecting the abnormal methodology for the acceleration measurements to study the safety and early-warning of the bridge. changes of the transverse acceleration measurements. Furthermore, the correlation models describing From their study, they found that the quadratic polynomial fitting provides a good capability for the overall structural behavior of the bridge can be obtained with the support of the health monitoring detecting the abnormal changes of the transverse acceleration measurements. Furthermore, the system, which includes cross-correlation models for accelerations. Liu et al. [6] concluded that the correlation models describing the overall structural behavior of the bridge can be obtained with the numerical simulation gives a good relation between the predicted and the measured responses. support of the health monitoring system, which includes cross-correlation models for accelerations. Therefore, the statistical analysis can be used to detect fatigue, torsion and reliability of structures Liu et al. [6] concluded that the numerical simulation gives a good relation between the predicted and basedthe measured on strain and response displacement s. Therefore measur , the ements statistica [16 l an –18 alysi ]. Furthermor s can be used to de e, parametric tect fatigue, models torsion areaused nd to reliability of structures based on strain and displacement measurements [16–18]. Furthermore, detect the performance of structures based on acceleration and strain measurements [19,20]. The main parametric models are used to detect the performance of structures based on acceleration and strain advantage of these methods is the ability to use them to evaluate and detect structural movements measurements [19,20]. The main advantage of these methods is the ability to use them to evaluate and damage. and detect structural movements and damage. The proposed study aims to evaluate Kaya bridge performance using a nondestructive monitoring The proposed study aims to evaluate Kaya bridge performance using a nondestructive system designed to assess the existing bridge under high-speed train movement as well as investigating monitoring system designed to assess the existing bridge under high-speed train movement as well the bridge structural behavior based on a simple application of time series and frequency analyses for as investigating the bridge structural behavior based on a simple application of time series and the acceleration and strain responses. Finally, the effectiveness of the monitoring sensors in time and frequency analyses for the acceleration and strain responses. Finally, the effectiveness of the frequency domains is assessed in order to decrease the monitoring system cost. monitoring sensors in time and frequency domains is assessed in order to decrease the monitoring system cost. 2. Kaya Bridge, High-Speed Trainsand Monitoring System Descriptions 2. Kaya Bridge, High-Speed Trainsand Monitoring System Descriptions Kaya Bridge, shown in Figure 1a, is a composite steel box girder bridge with 50 m span supports Kaya Bridge, shown in Figure 1a, is a composite steel box girder bridge with 50 m span supports two line high-speed railways. The cross-section of the bridge is shown in Figure 1b. Two longitudinal two line high-speed railways. The cross-section of the bridge is shown in Figure 1b. Two longitudinal girders are used with a spacing of 6.5 m and reinforced concrete deck with 14.00 m wide, which is used girders are used with a spacing of 6.5 m and reinforced concrete deck with 14.00 m wide, which is to provide the large stiffness for such heavy live loads. The use of a steel box as the deck system is used to provide the large stiffness for such heavy live loads. The use of a steel box as the deck system another feature in the design of this bridge, which is adopted to reduce uneven deﬂection and torsion is another feature in the design of this bridge, which is adopted to reduce uneven deflection and of the deck. The bridge design criteria at the mid span are presented in Table 1. torsion of the deck. The bridge design criteria at the mid span are presented in Table 1. (a) Figure 1. Cont. Appl. Sci. 2016, 6, 24 3 of 13 Appl. Sci. 2016, 6, 24 3 of 13 Appl. Sci. 2016, 6, 24 3 of 13 (b) Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). (b) Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). Table 1.Railway Bridge design criteria [5]. Table 1. Railway Bridge design criteria [5]. Table 1.Railway Bridge design criteria [5]. Review Factor Criteria Note Review VerticalFactor acceleration Criteria 4.9 m/s For concrete t Note rack Review Factor Criteria Note Displacement as safety 82 mm 2 Under 350 km/h Vertical acceleration 4.9 m/s 2 For concrete track Vertical acceleration 4.9 m/s For concrete track Displacement Displacement as a safety s comfort 822mm 2 mm Under Under 35 350 0 km/h km/h Displacement as safety 82 mm Under 350 km/h Displacement as comfort 22 mm Under 350 km/h Track twist 0.4 mm/m By dynamic analysis Displacement as comfort 22 mm Under 350 km/h Track twist 0.4 mm/m By dynamic analysis Track twist 0.4 mm/m By dynamic analysis The next-generation high-speed developed train (HEMU-430X) is intended to travel with a maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in The next-generation high-speed developed train (HEMU-430X) is intended to travel with The next-generation high-speed developed train (HEMU-430X) is intended to travel with a the passage track with four different speeds, i.e., 290, 360, 400, and 406 km/h. a maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in the passage the passa track ge tra with ck wifour th four dif differfent erent speeds, speeds, i.e. i.e. ,, 290, 290, 36 360, 0, 4 400, 00, and and 40 406 6 km km/h. /h. Figure 2. Axial spacing and loading of the high-speed train. Figure 2. Axial spacing and loading of the high-speed train. To monitor the beha Figure vior of 2. Axial the bri spacing dge as and per the loading passof ing o thef the high-speed high speed tr train.ains, a wireless SHM system was designed and installed on the Kaya Bridge shortly after it was opened to use the To monitor the behavior of the bridge as per the passing of the high speed trains, a wireless SHM HEMU-430X train, as shown in Figure 3. This on-line concise SHM system was designed with a To monitor the behavior of the bridge as per the passing of the high speed trains, a wireless system was designed and installed on the Kaya Bridge shortly after it was opened to use the minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and SHMHEMU- system 430 was X tra designed in, as shown i andninstalled Figure 3. Thi on the s onKaya -line concise Bridge SHM shortly system after was it de was sign opened ed with to a use strain gauge sensors (five sensors on each railway track)with sampling frequency of 100Hz were minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and the HEMU-430X train, as shown in Figure 3. This on-line concise SHM system was designed with installed on the main girders under each railway track in each direction with equal-spacing of 8.3 m. strain gauge sensors (five sensors on each railway track)with sampling frequency of 100Hz were a minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and The sensors were installed at the bottom flange of the main girders in the vertical direction to detect installed on the main girders under each railway track in each direction with equal-spacing of 8.3 m. strain gauge sensors (ﬁve sensors on each railway track) with sampling frequency of 100 Hz were and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire The sensors were installed at the bottom flange of the main girders in the vertical direction to detect installed system cons on the main ists of gir a ders set ofunder sensors each , data railway acquistrack ition, dat in each a tran dir smiss ection ion, with data equal-spacing management and of a 8.3 m. and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire structural evaluation mechanism. The primary purpose of the system is to monitor in-service The sensors were installed at the bottom ﬂange of the main girders in the vertical direction to detect system consists of a set of sensors, data acquisition, data transmission, data management and a performance of the bridge structure under high-speed trains, and to provide early warning of and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire system structural evaluation mechanism. The primary purpose of the system is to monitor in-service abnormal changes in in-service performance of the bridge. Herein, the structural parameters to be consists of a set of sensors, data acquisition, data transmission, data management and a structural performance of the bridge structure under high-speed trains, and to provide early warning of monitored in the Kaya Bridge were determined using the structural sensitivity analysis with the finite evaluation mechanism. The primary purpose of the system is to monitor in-service performance of abnormal changes in in-service performance of the bridge. Herein, the structural parameters to be element model under the designed train speeds action, as shown in Kim et al. [5].Other important monitored in the Kaya Bridge were determined using the structural sensitivity analysis with the finite the bridge structure under high-speed trains, and to provide early warning of abnormal changes in parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge element model under the designed train speeds action, as shown in Kim et al. [5].Other important in-service performance of the bridge. Herein, the structural parameters to be monitored in the Kaya were monitored and studied under different speeds of the passing high-speed trains. parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge Bridge were determined using the structural sensitivity analysis with the ﬁnite element model under were monitored and studied under different speeds of the passing high-speed trains. the designed train speeds action, as shown in Kim et al. [5]. Other important parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge were monitored and studied under different speeds of the passing high-speed trains. Appl. Sci. 2016, 6, 24 4 of 13 Appl. Sci. 2016, 6, 24 4 of 13 Appl. Sci. 2016, 6, 24 4 of 13 Figure 3. Structural health monitoring (SHM) system locations and components. Figure 3. Structural health monitoring (SHM) system locations and components. 3. Evaluation of the Bridge Condition Using SHM Monitoring Data 3. Evaluation of the Bridge Condition Using SHM Monitoring Data Kim et al. [5] designed a 3-D finite element model (FEM) of the Kaya Bridge using ANSYS V10.0 Figure 3. Structural health monitoring (SHM) system locations and components. software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of Kim et al. [5] designed a 3-D ﬁnite element model (FEM) of the Kaya Bridge using ANSYS V10.0 different train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are 3. Evaluation of the Bridge Condition Using SHM Monitoring Data software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of different concluded as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are concluded bendi Kim ng) et al. an d 5.913 (1 [5] designed st torsi a 3-o D fin n) Hz ite e folr the ementf model irst, second (FEM) o and thi f ther Ka d modes, respecti ya Bridge using A velyN ; SYS (ii) the V10.0 as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd bending) maximum vertical acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of and 5.913 250 (1st and torsion) 280 km/Hz h, resp forec the tive ﬁrst, ly; (isecond ii) the m and aximthir um accel d modes, eration respecti and dvely; isplace (ii) ment the for t maximum he train vertical different train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are 400 km/h speed are 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the concluded as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed 250 and 280 km/h, bridge design criteria in Table 1 shows that the bridge is safe under applied loads. bending) and 5.913 (1st torsion) Hz for the first, second and third modes, respectively; (ii) the respectively; (iii) the maximum acceleration and displacement for the train 400 km/h speed are The current study utilizes the real monitoring data that is collected from the SHM system of maximum vertical acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the bridge design criteria Kaya Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, 250 and 280 km/h, respectively; (iii) the maximum acceleration and displacement for the train in Table 1 shows that the bridge is safe under applied loads. torsional and fatigue behavior of the steel deck as well as evaluating and comparing the frequency 400 km/h speed are 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the The current study utilizes the real monitoring data that is collected from the SHM system of Kaya contents for the measurements of the monitoring sensors. bridge design criteria in Table 1 shows that the bridge is safe under applied loads. Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, torsional The current study utilizes the real monitoring data that is collected from the SHM system of 3.1. Evaluation of the Bridge Girder Vibration Behavior and fatigue behavior of the steel deck as well as evaluating and comparing the frequency contents for Kaya Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring the measurements of the monitoring sensors. torsional and fatigue behavior of the steel deck as well as evaluating and comparing the frequency points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, contents for the measurements of the monitoring sensors. respectively. It can be seen that the dt, (dt = t2 (leave time) − t1 (entrance time)) values are reported as 3.1. Evaluation of the Bridge Girder Vibration Behavior 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that the 3.1. Evaluation of the Bridge Girder Vibration Behavior Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring vibration time effect on the bridge decreased by 68.65% as the train speed increased from Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring 290 to 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, 290 km/h are approximately the same, while as the speed increased, the entrance and exit monitoring respectively. It can be seen that the dt, (dt = t (leave time) t (entrance time)) values are reported 2 1 respecti points are ex vely. It ca periencin n be seen tha g high vibr t the ation r dt, (dt esponse com = t2 (leave time) pared to the − t1 (entr mid sp ance time an point. )) values are reported as as 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that the the vibration time effect on the bridge decreased by 68.65% as the train speed increased from 290 to vibration time effect on the bridge decreased by 68.65% as the train speed increased from 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of 290 km/h 290 to 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of are approximately the same, while as the speed increased, the entrance and exit monitoring points are 290 km/h are approximately the same, while as the speed increased, the entrance and exit monitoring experiencing high vibration response compared to the mid span point. points are experiencing high vibration response compared to the mid span point. (a) (b) Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a)passage response points for 290 km/h; (b) passage response points for 406 km/h. (a) (b) Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a)passage Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a) passage response points for 290 km/h; (b) passage response points for 406 km/h. response points for 290 km/h; (b) passage response points for 406 km/h. Appl. Sci. 2016, 6, 24 5 of 13 Appl. Sci. 2016, 6, 24 5 of 13 Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is much Appl.smaller Sci. 2016, 6than , 24 that of 290 km/h. In addition, it is noticed that the vibration response 5 of 13 for the Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, much smaller than that of 290 km/h. In addition, it is noticed that the vibration response for the Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is the acceleration responses for the passage and opposite sides are equal. These indicate that, although passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, much smaller than that of 290 km/h. In addition, it is noticed that the vibration response for the the structural layouts of the ﬁve monitoring points of the main girder are the same, there is a signiﬁcant the acceleration responses for the passage and opposite sides are equal. These indicate that, although passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, the structural layouts of the five monitoring points of the main girder are the same, there is a difference between the vertical vibration characteristics of two sides of the bridge due to the rail the acceleration responses for the passage and opposite sides are equal. These indicate that, although significant difference between the vertical vibration characteristics of two sides of the bridge due to irregularity in the vertical directions with different speeds. Thus, there is a need to monitor the vertical the structural layouts of the five monitoring points of the main girder are the same, there is a the rail irregularity in the vertical directions with different speeds. Thus, there is a need to monitor accelerations of the span in the long term so as to realize anomaly alarms for vibration behavior significant difference between the vertical vibration characteristics of two sides of the bridge due to the vertical accelerations of the span in the long term so as to realize anomaly alarms for vibration of the the rail main irre girg der ularity .In addition, in the vertical d the measur irections w ed vertical ith different speed acceleration s. Thus, ther is smaller e is a n than eed to mo the FEM nitor results behavior of the main girder.In addition, the measured vertical acceleration is smaller than the FEM the vertical accelerations of the span in the long term so as to realize anomaly alarms for vibration by 62% according to the design criteria of the bridge (as shown Table 1),which means that the real results by 62% according to the design criteria of the bridge (as shown Table 1),which means that the behavior of the main girder.In addition, the measured vertical acceleration is smaller than the FEM response is safe. Furthermore, the girder torsional behavior should be studied with new development real response is safe. Furthermore, the girder torsional behavior should be studied with new results by 62% according to the design criteria of the bridge (as shown Table 1),which means that the trains’ speeds. development trains’ speeds. real response is safe. Furthermore, the girder torsional behavior should be studied with new development trains’ speeds. 0.8 0.8 290 290 360 360 0.6 0.6 0.8 400 0.8 400 290 40 296 0 0.4 0.4 0.6 0.6 400 400 0.2 0.2 406 406 0.4 0.4 0.2 0.2 -0.2 -0.2 0 0 -0.4 -0.4 -0.2 -0.2 -0.6 -0.6 -0.4 -0.4 -0.8 -0.8 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -0.6 -0.6 Time (Sec.) Time (Sec.) -0.8 -0.8 (a) (b) 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) Figure 5. Vertical acceleration (a) ( time histories of the passage and opposite mid bspan girder point. ( ) a) Figure 5. Vertical acceleration time histories of the passage and opposite mid span girder point. passage response of mid span point; (b) opposite response of mid span point. Figure 5. Vertical acceleration time histories of the passage and opposite mid span girder point. (a) (a) passage response of mid span point; (b) opposite response of mid span point. passage response of mid span point; (b) opposite response of mid span point. Figure 6 illustrates the dynamic torsional behavior of the bridge girder based on vertical Figur accelerat e 6ion illustrates measurem the entsdynamic [21].The t torsional orsional bbehavior ehavior of t ofhthe e girde bridge r due gir to der train p based asson age vertical at Figure 6 illustrates the dynamic torsional behavior of the bridge girder based on vertical 406 km/h is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds acceleration measurements [21].The torsional behavior of the girder due to train passage at 406 km/h acceleration measurements [21].The torsional behavior of the girder due to train passage at is shown in Figure 6b. is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds is shown 406 km/h is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds a −a in Figure 6b. is shown in Figure 6b. T= (1) a a pass opps a −a T (1) T= (1) where T, a ,a and l are the torsion, acceleration for the passage and opposite points and distances between sensors’ positions. where T, a , a and l are the torsion, acceleration for the passage and opposite points and distances pass opps where T, a ,a and l are the torsion, acceleration for the passage and opposite points and between sensors’ positions. distance0. s bet 5 ween sensors’ positions. 0.05 P1 -0.5 0.50 2 4 6 8 10 12 14 0.05 -0.05 0.1 0 2 4 6 8 10 12 14 P1 290 0.1 P2 -0.5 0 2 4 6 8 10 12 14 -0.1 -0.05 0.10 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 0.2 0.1 P2 -0.1 P3 0 2 4 6 8 10 12 14 0 360 -0.1 0 0.2 0 2 4 6 8 10 12 14 -0.2 0.2 400 0 2 4 6 8 10 12 14 -0.0 1 0.2 P3 0 2 4 6 8 10 12 14 P4 0.2 -0.2 -0.2 0 2 4 6 8 10 12 40014 0 2 4 6 8 10 12 14 -0.2 0.2 0.20 2 4 6 8 10 12 14 P4 406 -0.0 2 P5 0 0 2 4 6 8 10 12 14 -0.2 0.2 0 2 4 6 8 10 12 14 -0.2 -1 10 2 4 6 8 10 12 14 0 2 4 6 8 10 12 40614 Time (sec) Time (sec) P5 -1 -0.2 (a) (b) 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (sec) Time (sec) Figure 6. Torsional measurements fo (a) ( r the bridge girder. (a)the torsional of the monitorin b) g points at 406 km/h passage; (b)mid span point torsional values. Figure 6. Torsional measurements for the bridge girder. (a)the torsional of the monitoring points at Figure 6. Torsional measurements for the bridge girder. (a) the torsional of the monitoring points at 406 km/h passage; (b)mid span point torsional values. It is noticed that the torsional values increased as the train velocity increased with maximum 406 km/h passage; (b) mid span point torsional values. values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased It is noticed that the torsional values increased as the train velocity increased with maximum values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased It is noticed that the torsional values increased as the train velocity increased with maximum values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased by 2 -2 2 -2 Torsional (rad/s ) Acc (ms ) Torsional (rad/s ) Acc (ms ) 2 -2 -2 Torsio 2nal (rad/s ) Acc (ms ) Torsional (rad/s ) Acc (ms ) Appl. Sci. 2016, 6, 24 6 of 13 61.20%, while they increased by 22.33% as the train speed increased from 360 to 406 km/h. This means Appl. Sci. 2016, 6, 24 6 of 13 that the bridge deck torsional response is higher than the vibration response. The girder torsional value at the mid span point, 0.103 rad/s , is within the design values, as shown in Table 1. by 61.20%, while they increased by 22.33% as the train speed increased from 360 to 406 km/h. This The statistical parameters, maximum and root mean square (RMS), for the acceleration means that the bridge deck torsional response is higher than the vibration response. The girder measur toements rsional vand alue at torsional the mid calculations span point, 0. at 10360 3 rad/ and s , 406 is wit km/h hin thar e de e pr sign esented valuesin , asT shown able 2.in T The able 1 360 km/h . The statistical parameters, maximum and root mean square (RMS), for the acceleration is assumed as the base-speed for the study of the bridge safety because the bridge design speed is measurements and torsional calculations at 360 and 406 km/h are presented in Table 2. The 360 km/h 350 km/h. Furthermore, the Bessel ﬁlter cut-off frequency of 30 Hz is applied to remove the noise is assumed as the base-speed for the study of the bridge safety because the bridge design speed is measurements of the accelerometer [21]. It is noticed that the maximum acceleration occurred at points 350 km/h. Furthermore, the Bessel filter cut-off frequency of 30 Hz is applied to remove the noise 1 and 5 for the passage and opposite directions at 406 km/h speed, respectively, with values within the measurements of the accelerometer [21]. It is noticed that the maximum acceleration occurred at design limits as shown in Table 1. While there is no high relative change at points 2 to 4, the RMS of points 1 and 5 for the passage and opposite directions at 406 km/h speed, respectively, with values the 360 km/h is smaller than that of the 406 km/h on passage way, while vice versa in the opposite within the design limits as shown in Table 1. While there is no high relative change at points 2 to 4, way except for point 5. The maximum torsion occurred at point 5 at a speed of 406 km/h with a value the RMS of the 360 km/h is smaller than that of the 406 km/h on passage way, while vice versa in the very close to the design value (Table 1). Thus, it is recommended to limit the train speed to 400 km/h opposite way except for point 5. The maximum torsion occurred at point 5 at a speed of 406 km/h only. In with addition, a value very cl the effective ose to the design v torsion test alue should (Tablebe 1). assessed Thus, it is rec at the ommended t end points o limit of the the tr bridge ain speed (point of to 400 km/h only. In addition, the effective torsion test should be assessed at the end points of the maximum torsion). bridge (point of maximum torsion). Table 2. Maximum acceleration, root mean square (RMS) and torsional acceleration for the ﬁltration of Table 2. Maximum acceleration, root mean square (RMS) and torsional acceleration for the filtration vibration measurements. of vibration measurements. 2 2 2 2 Acceleration (m/s ) RMS (m/s ) Max Torsion Acceleration (m/s)RMS(m/s ) Max (rad/s ) Passage Opposite Passage Opposite Torsion Point Point Passage Opposite Passage Opposite (rad/s ) 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 1 0.104 1.251 0.196 0.079 0.006 0.035 0.008 0.004 0.059 0.191 1 0.104 1.251 0.196 0.079 0.006 0.035 0.008 0.004 0.059 0.191 2 0.169 0.183 0.231 0.088 0.008 0.009 0.011 0.006 0.072 0.191 3 0.244 0.319 0.279 0.146 0.010 0.012 0.013 0.007 0.079 0.037 2 0.169 0.183 0.231 0.088 0.008 0.009 0.011 0.006 0.072 0.191 4 0.569 0.622 0.252 0.239 0.014 0.016 0.012 0.008 0.244 0.055 3 0.244 0.319 0.279 0.146 0.010 0.012 0.013 0.007 0.079 0.037 5 - 0.401 0.169 2.497 - 0.013 0.008 0.035 - 0.343 4 0.569 0.622 0.252 0.239 0.014 0.016 0.012 0.008 0.244 0.055 5 - 0.401 0.169 2.497 - 0.013 0.008 0.035 - 0.343 Ding et al. [3,17] concluded that the quadratic linear ﬁtting for the maximum and RMS are Ding et al. [3,17] concluded that the quadratic linear fitting for the maximum and RMS are calculated for the acceleration measurements can be used to detect the performance of high-speed calculated for the acceleration measurements can be used to detect the performance of high-speed railway bridges based on one span monitoring. Herein, this method is applied to detect and check railway bridges based on one span monitoring. Herein, this method is applied to detect and check the performance of the bridge due to different speeds on the two ways of the track. The maximum the performance of the bridge due to different speeds on the two ways of the track. The maximum and RMS of 150 acceleration of the original measurements are shown in Figure 7. The quadratic and RMS of 150 acceleration of the original measurements are shown in Figure 7. The quadratic linear linear ﬁtting is suitable for the maximum acceleration (Figure 7a), while the RMS shows no correlation fitting is suitable for the maximum acceleration (Figure 7a), while the RMS shows no correlation between the passage and opposite ways. Therefore, the maximum acceleration ﬁtting can be used to between the passage and opposite ways. Therefore, the maximum acceleration fitting can be used to check the safety of the bridge. check the safety of the bridge. 0.6 -3 x 10 y = - 0.34*x + 1.2*x - 0.001 0.5 0.4 0.3 0.2 0.1 -0.1 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0 1 2 3 4 5 6 7 8 -2 -2 -3 Acc Max. of Pass (ms ) RMS of Acc Pass (ms ) x 10 (a) (b) Figure 7. Cross-correlation between the passage and opposite way responses for 360 km/h. (a) Figure 7. Cross-correlation between the passage and opposite way responses for 360 km/h. cross-correlation maximum acceleration; (b) cross-correlation roote mean square (RMS) acceleration. (a) cross-correlation maximum acceleration; (b) cross-correlation roote mean square (RMS) acceleration. -2 Acc Max. of Opp (ms ) -2 RMS of Acc Opp (ms ) Appl. Sci. 2016, 6, 24 7 of 13 In this case, the ﬁtting equation is used to predict the opposite way measurements for the 400 and 406 km/h, as shown in Figure 8. The correlation coefﬁcient of the measured maximum values and Appl. Sci. 2016, 6, 24 7 of 13 predicted values with 400 and 406 km/h is 0.999 and 0.998 for opposite accelerations, respectively. In this case, the fitting equation is used to predict the opposite way measurements for the 400 The results indicate that good cross-correlation exists between the maximum values of the opposite and 406 km/h, as shown in Figure 8. The correlation coefficient of the measured maximum values way at the two train speeds. Herein, the control chart can be used to monitor the changes in the and predicted values with 400 and 406 km/h is 0.999 and 0.998 for opposite accelerations, respectively. vertical accelerations caused by deterioration of the vibration behavior. Firstly, the condition index The results indicate that good cross-correlation exists between the maximum values of the opposite e (e = Max (measurements) Max (prediction)) for early warning of abnormal vibration behavior is way at the two train speeds. Herein, the control chart can be used to monitor the changes in the deﬁned vert as ica the l accel differ erat ence ions c between aused by det the e measur rioration o ed fand the vibrat predicted ion beh maximum avior. Firstlvalues y, the condit of the ion accelerations index e in (e = Max (measurements) − Max (prediction)) for early warning of abnormal vibration behavior is the opposite way. Then, a mean value control chart is employed to monitor the time series of e with defined as the difference between the measured and predicted maximum values of the accelerations regard to the opposite accelerations. For online monitoring, the controlling parameter is chosen so in the opposite way. Then, a mean value control chart is employed to monitor the time series of e with that, when the structural vibration is in good condition, all observation samples fall between the regard to the opposite accelerations. For online monitoring, the controlling parameter is chosen so control limits. When the new measurement is made, the structural abnormal vibration condition can that, when the structural vibration is in good condition, all observation samples fall between the be detected if an unusual number of samples fall beyond the control limits. In this study, the control control limits. When the new measurement is made, the structural abnormal vibration condition can condition be det is assumed ected if an with unusu 290 al nu km/h mber o found f sampwithin les fall beyo 0.025 nd t to he cont 0.03 rom/s l limit.s.The In th calculated is study, the condition control index condition is assumed with 290 km/h found within 0.025 to −0.03 m/s . The calculated condition index for the 400 and 406 km/h is between 0.0043 and 0.028 m/s . Hence, a long-term monitoring of the for the 400 and 406 km/h is between 0.0043 and −0.028 m/s . Hence, a long-term monitoring of the maximum values of the opposite accelerations can help in the early-warning of the vibration behavior maximum values of the opposite accelerations can help in the early-warning of the vibration behavior deterioration. These results are identical with the Ding et al. [3,17] conclusions. deterioration. These results are identical with the Ding et al. [3,17] conclusions. (a) (b) Figure 8. Predicting the effect of cross-correlation between maximum values of accelerations by using Figure 8. Predicting the effect of cross-correlation between maximum values of accelerations by using quadratic polynomials. (a) 400 km/h; (b) 406 km/h. quadratic polynomials. (a) 400 km/h; (b) 406 km/h. 3.2. Evaluation of the Bridge Girders’ Train Responses 3.2. Evaluation of the Bridge Girders’ Train Responses The measured vertical strain histories of the monitoring points of the passage way for the train speeds 290 and 406 km/h are presented in Figure 9a,b. The vertical strain of the mid-span point of The measured vertical strain histories of the monitoring points of the passage way for the train passage and opposite ways for different train speeds are compared in Figure 9c,d. As the train speeds speeds 290 and 406 km/h are presented in Figure 9a,b. The vertical strain of the mid-span point of increase to 290, 360, 400 and 406 km/h, the strain responses (dt) decrease to 2.1, 1.93, 1.64 and 1.61 s, passage and opposite ways for different train speeds are compared in Figure 9c,d. As the train speeds respectively. This shows that the time of static strain responses decreases by 23.33% when the speed increase change to 290, s fr360, om 29 400 0 and and 40406 6 km km/h, /h. In add theitstrain ion, the r m esponses aximum s (dt train r ) decr esp ease onse to in t 2.1, he p 1.93, assa1.64 ge and and 1.61 s, opposite ways occurred at a speed of 290 km/h. The results as such show that the static and dynamic respectively. This shows that the time of static strain responses decreases by 23.33% when the speed behavior of the bridge is higher with low train speeds. In addition, the strain measurements of the changes from 290 and 406 km/h. In addition, the maximum strain response in the passage and opposite bridge points are highly correlated (0.95) with each speed change. This means that the strain ways occurred at a speed of 290 km/h. The results as such show that the static and dynamic behavior measurement of the mid span point can be used to detect the performance of the whole bridge. This of the bridge is higher with low train speeds. In addition, the strain measurements of the bridge points situation will decrease the cost of the monitoring system due to the use of one monitoring point only. are highly correlated (0.95) with each speed change. This means that the strain measurement of the mid The high correlation (0.99) of strain response for the passage and opposite ways occurred at 400 and 406 km/h. It means that the strain response of the two speeds is approximately equal in the static span point can be used to detect the performance of the whole bridge. This situation will decrease the response. However, to show clearly the relationship between dynamic and static response of the cost of the monitoring system due to the use of one monitoring point only. The high correlation (0.99) bridge, the dynamic increment factor should be calculated and analyzed. The Savitzky-Golay finite of strain response for the passage and opposite ways occurred at 400 and 406 km/h. It means that impulse response(FIR) smoothing filter is applied to detect the static strain of the bridge. The first the strain response of the two speeds is approximately equal in the static response. However, to show polynomial order with 101 frame size is utilized in this study. Figure 10a shows the strain clearly the relationship between dynamic and static response of the bridge, the dynamic increment measurements and filter data of the mid span of passage direction with a train speed of 406 km/h. factor should be calculated and analyzed. The Savitzky-Golay ﬁnite impulse response(FIR) smoothing Therefore, the dynamic increment factor (DF) can be calculated as follows [22,23]: ﬁlter is applied to detect the static strain of the bridge. The ﬁrst polynomial order with 101 frame size is utilized in this study. Figure 10a shows the strain measurements and ﬁlter data of the mid span of Appl. Sci. 2016, 6, 24 8 of 13 passage direction with a train speed of 406 km/h. Therefore, the dynamic increment factor (DF) can be Appl. Sci. 2016, 6, 24 8 of 13 calculated as follows [22,23]: dyn DF 1 A (2) DF = 1+ A (2) stc where A and A are the maximum absolute of dynamic and static amplitude of the strain, as shown Appl. Sci. 2016, 6, 24 8 of 13 dyn stc where A and A are the maximum absolute of dynamic and static amplitude of the strain, as in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the monitored points shown in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the are illustrated in Figure 10b. DF = 1+ (2) monitored points are illustrated in Figure 10b. where A 15 and A are the maximum abso lute of dynamic and st 5 atic amplitude of the strain, as shown in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the monitored points are illustrated in Figure 10b. 0 -5 15 5 -5 -10 -10 -15 -15 P1 P1 P2 0 -5 P2 -20 P3 P3 -20 P4 -5 P4 -25 P5 -10 P5 -10 -30 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -15 -15 Time (Sec.) Time (Sec.) P1 P1 P2 P2 -20 P3 (a) (b) P3 -20 P4 P4 -25 P5 P5 10 15 -30 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) (a) (b) 10 15 -5 -5 -10 -10 -15 0 -15 290 -5 290 -5 -20 400 -20 -10 -10 -25 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -15 -15 Time (Sec.) 290 Time (Sec.) -20 400 400 -20 (c) (d) 406 -25 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response (c) (d) points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; (d) opposite response mid span points. Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response (d) opposite response mid span points. points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; The dynamic factor calculation shows that the DF of 290 km/h train is higher than other train (d) opposite response mid span points. speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher The dynamic factor calculation shows that the DF of 290 km/h train is higher than other train than the passa The dynamic ge di factor rectio calculation n with all tr shows t ain speeds hat the except point (P DF of 290 km/h trai 3) with speeds o n is higher t f 400 an han other trai d 406 km n /h. speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher than the passage direction with all train speeds except point (P3) with speeds of 400 and 406 km/h. than the passage direction with all train speeds except point (P3) with speeds of 400 and 406 km/h. (a) (b) (a) (b) Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and filtered static strain; (b) dynamic factor of the monitoring points. Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and filtered static Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and ﬁltered static strain; (b) dynamic factor of the monitoring points. strain; (b) dynamic factor of the monitoring points. From Figure 10b, it can be seen that the DFs for the development speeds are less than two. It means From that F th igu e dom re 10b inant , it c p an e b rform e seen ance th o at t f thh ee DFs brid for ge the is stdevelopment speed atic with speeds ofs 36 are 0, 4 less th 00 and an two. It 406 km/h at means that the dominant performance of the bridge is static with speeds of 360, 400 and 406 km/h at all monitoring points, while the dynamic performance occurred at the opposite monitoring direction From Figure 10b, it can be seen that the DFs for the development speeds are less than two. It means all monitoring points, while the dynamic performance occurred at the opposite monitoring direction with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the that the dominant performance of the bridge is static with speeds of 360, 400 and 406 km/h at all with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the strain ( s) Strain (μs) strain ( s) Strain (μs) Strain (μs) strain (μs) Strain (μs) strain (μs) Appl. Sci. 2016, 6, 24 9 of 13 monitoring points, while the dynamic performance occurred at the opposite monitoring direction with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior should be studied to investigate the safety of the bridge under high train speed effect. Appl. Sci. 2016, 6, 24 9 of 13 Herein, the cross-correlation evaluation is used to predict the dynamic behavior of strain static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior contents. The same conditions used in the acceleration analysis are used in this part. Figure 11 should be studied to investigate the safety of the bridge under high train speed effect. presents the cross-correlation and the cubic ﬁtting of the maximum dynamic of strain measurements. Herein, the cross-correlation evaluation is used to predict the dynamic behavior of strain Appl. Sci. 2016, 6, 24 9 of 13 The relationship between the maximum dynamic of strain contents in the passage and opposite contents. The same conditions used in the acceleration analysis are used in this part. Figure 11 static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior presents the cross-correlation and the cubic fitting of the maximum dynamic of strain measurements. directions for the train speed of 360 km/h is illustrated in Figure 11a. While Figure 11b shows the should be The relation studship between ied to investigate the maxim the safetu ym dyn of the brid amic o gef unde strain r high conte train nts in speed the p effect. assage and opposite prediction of the opposite direction contents of the dynamic strain for 406 km/h, the statistical analysis direct Herei ionns, the cross- for the train correla speed tion eval of 360 km/ uatih on i is il s used lustra to predi ted in Figure ct the 11a. dyna Whm ile Fi ic beha gurevi 11b or of strain shows the of cubic and quadratic ﬁtting shows that the correlation coefﬁcient of quadratic is 0.90, so the cubic cont predi entsc. The tion of the opposi same conditions te di u rect sed io in n contents of the acceler the dyna ation analmi ysc is stra are in used for 406 in t km/h, the sta his part. Figurtei s11 tica l ﬁtting in presents the cross- this case is better correla to tion a predict nd the cubi the dynamic c fitting of th behavior e maximum of dyna the mstrain ic of stracontents. in measureme The nts.comparison analysis of cubic and quadratic fitting shows that the correlation coefficient of quadratic is 0.90, so The relationship between the maximum dynamic of strain contents in the passage and opposite the cubic fitting in this case is better to predict the dynamic behavior of the strain contents. The between the results of acceleration and strain dynamic contents shows that the dynamic evaluation of direct compa ionrsi for son b th ee tween the resul train speed oft 3 s of 60 a km/ ccelh e ra isti il on lust an ra d stra ted in Fi in dyna gure m 11 ic a. contents shows tha While Figure 11b t the dyna shows thmi e c acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents predi evac lua tion of the opposi tion of acceleratite di on isrect better ion contents of and effective to the dyna assess the vi mic stra brati in f oo n of the bri r 406 km/h, the sta dge, but the tisti stra cal in can be used to decrease the monitoring cost. andynam alysis of cubi ic contents can c and qua be used to dratic fitti dec ng shows tha rease the monito t the correla ring coti st. on coefficient of quadratic is 0.90, so the cubic fitting in this case is better to predict the dynamic behavior of the strain contents. The 7 18 comparison between the results of acceleration and strain dynamic contents shows that the dynamic 3 2 R=0.94 y = 0.1*x - 0.87*x + 2.9*x - 0.44 evaluation of acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents can be used to decrease the monitoring cost. 7 18 3 8 3 2 R=0.94 y = 0.1*x - 0.87*x + 2.9*x - 0.44 3 8 -1 -2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 1 2 3 4 5 6 7 8 Max. of Str. Pass (μs) Max Str. measured (μs) (a) (b) Figure 11. Cross-correlation and prediction of maximum strain dynamic. (a) cross-correlation Figure 11. -1 Cross-correlation and prediction of maximum -2 strain dynamic. (a) cross-correlation maxi 0 mu 0.5 m d 1 y 1.n 5ami 2 c of s 2.5 trai 3 n3. ; 5(b) 4predi 4.5 cting 5 effect of cross- 0correlation. 1 2 3 4 5 6 7 8 Max. of Str. Pass ( s) Max Str. measured ( s) μ μ maximum dynamic of strain; (b) predicting effect of cross-correlation. (a) (b) To perform fatigue evaluation, a simplified rain-flow cycle counting algorithm was used first to process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. Figure 11. Cross-correlation and prediction of maximum strain dynamic. (a) cross-correlation To perform fatigue evaluation, a simpliﬁed rain-ﬂow cycle counting algorithm was used ﬁrst to The spectra of stress matrix calculated using the strain history data with train speeds of 360 and maximum dynamic of strain; (b) predicting effect of cross-correlation. process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. 406 km/h for the passage (left) and opposite (right) as shown in Figure 12, respectively.In addition, the maximum stress c To perform fatigue eval ycles ar uation, e prese a simpli nted in F fied raiin gure -flow cycl 12. It is observed tha e counting algorithm wa t the ma s used ximum stress first to The spectra of stress matrix calculated using the strain history data with train speeds of 360 and process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. amplitude, obtained from strain history curves under two trains’ effects for the passage and opposite 406 km/h for the passage (left) and opposite (right) as shown in Figure 12, respectively. In addition, The spectra of stress matrix calculated using the strain history data with train speeds of 360 and directions, is smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher the maximum stress cycles are presented in Figure 12. It is observed that the maximum stress amplitude, 40stress 6 km/h rang for e. thMost cyc e passage les o (left ccur ) anin the re d opposit gion e (right of st) re as ss m shown in F ean and am igup re 1 litude 2, resp from ec t− iv 0.5 to 0.5 MPa ely.In addition, an d obtained from strain history curves under two trains’ effects for the passage and opposite directions, is the maximum stress c 0 to 0.5 MPa, respectiv yecles ar ly. Thus, e prese the m nted in F ean and iam gure pl 12 itude v . It is observed tha alues of the stress t the ma for the tximum stress wo trains in the amplitude, obtained from strain history curves under two trains’ effects for the passage and opposite two directions are equal. smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher stress range. directions, is smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher Most cycles occur in the region of stress mean and amplitude from 0.5 to 0.5 MPa and 0 to 0.5 MPa, stress range. Most cycles occur in the region of stress mean and amplitude from −0.5 to 0.5 MPa and respectively. Thus, the mean and amplitude values of the stress for the two trains in the two directions 0 to 0.5 MPa, respectively. Thus, the mean and amplitude values of the stress for the two trains in the are equal. 150 two directions are equal. X: 1.24e-005 Y: -0.06603 Z: 164 -2.5 100 2 -2 X: 1.24e-005 -1.5 1.5 Y: -0.06603 -1 Z: 164 -0.5 0.5 Mean (MPa) 0 Amplitude (MPa) -2.5 (a) -2 2 -1.5 1.5 -1 -0.5 0.5 Mean (MPa) Amplitude (MPa) (a) Figure 12. Cont. Number of cycles Max. of Str. Opp ( s) Number of cycles Max. of Str. Opp (μs) Max Str. predicted (μs) Max Str. predicted (μs) Appl. Sci. 2016, 6, 24 10 of 13 Appl. Sci. 2016, 6, 24 10 of 13 X: 3.828e-006 X: 4.496e-005 Y: -0.02062 Y : 0.4223 Z: 126.5 50 Z: 132 0 0 -2 -3 -1.5 2.5 1.2 -2 2 1 -1 0.8 -1 1.5 0.6 -0.5 0.4 0.5 0.2 Mean (MPa) Mean (MPa) Amplitude (MPa) Amplitude (MPa) (b) Figure 12. Rain-flow matrix of mid-span stress (a) 360 km/h; (b) 406 km/h. Figure 12. Rain-ﬂow matrix of mid-span stress (a) 360 km/h; (b) 406 km/h. The maximum number of stress cycles at 406 km/h is smaller than that occurring at 360 km/h in the two directions. The results show that the fatigue stress and number of cycles limit are 29 MPa and The maximum number of stress cycles at 406 km/h is smaller than that occurring at 360 km/h 2 × 10 , respectively [25], as recommended by Eurocode 3. The value of the equivalent stress in the two directions. The results show that the fatigue stress and number of cycles limit are 29 MPa amplitude and the number of cycles when the high-speed train passes through bridge is far less than and 2 10 thi,sr val espectively ue for two tra [25 ins. ],However, the fa as recommended tigue beha byvEur ior of ocode the bri3. dge deck The value satisfies the req of the equivalent uirement stress of the infinite-fatigue-life design method. amplitude and the number of cycles when the high-speed train passes through bridge is far less than this value for two trains. However, the fatigue behavior of the bridge deck satisﬁes the requirement of 3.3. Acceleration-Strain Frequency Domain Evaluation the inﬁnite-fatigue-life design method. The frequency contents of strain and acceleration measurements for the mid-span monitoring points in the passage and opposite directions are illustrated in Figure 13. The cross spectrum density 3.3. Acceleration-Strain Frequency Domain Evaluation function in Matlab (Version 7.6, MathWorks, Natics, MA, USA, 2008) is used to calculate the frequency contents.Based on the FEM [5] analysis, the band-pass filters in between 1 to 45 Hz with The frequency contents of strain and acceleration measurements for the mid-span monitoring 101 hamming window are used to filter the measured data to include the static and dynamic points in the passage and opposite directions are illustrated in Figure 13. The cross spectrum density frequency contents of the bridge. From Figure 13, the frequency contents are 3.223, 3.906 Hz and 3.223, function in4.Matl 199 Hz abfor (V29 ersion 0 and 7.6, 360 kMathW m/h at th orks, e oppo Natics, site and p MA, assage d USA, irect 2008) ions, re issp used ectiveto ly. In calculate additionthe , the frequency frequency contents equal (4.297 Hz) for the 400 and 406 km/h at the two directions. From the contents.Based on the FEM [5] analysis, the band-pass ﬁlters in between 1 to 45 Hz with 101 hamming comparison of the FEM frequency and real data, it is observed that the first dynamic mode changes window are used to ﬁlter the measured data to include the static and dynamic frequency contents of increased with increasing the trains’ speeds. The changes of passage frequency from the first bending the bridge. From Figure 13, the frequency contents are 3.223, 3.906 Hz and 3.223, 4.199 Hz for 290 and FEM frequency mode are 18.5%, 24.2%, 25.8% for the speeds 290, 360 and 406 km/h, respectively. The 360 km/h strain at the freque opposite ncies contents and passage at the two dir direction ections, s w riespectively th the effect of . In all tr addition, ains’ speeds are the frsimilar. In equency contents addition, the static frequency contents are clearly shown with strain measurements only. The low equal (4.297 Hz) for the 400 and 406 km/h at the two directions. From the comparison of the FEM frequencies are 0.781, 0.977, 1.172, 1.172 Hz and 0.879, 1.074, 1.172, 1.172 Hz of the opposite and frequency and real data, it is observed that the ﬁrst dynamic mode changes increased with increasing passage directions for the 290, 360, 400, 406 km/h train speeds, respectively. It means that the strain the trains’ speeds. The changes of passage frequency from the ﬁrst bending FEM frequency mode are measurements are enough to estimate the static and dynamic behavior in frequency domain. 18.5%, 24.2%, Moreover, f 25.8% for rom the compa the speeds rison between the fi 290, 360 and 406 rst km/h, mode contents respectively of the me . The asurements and strain frequencies the FEM contents calculations, it can be concluded that the bridge is safe under its current dynamic behavior with the at the two directions with the effect of all trains’ speeds are similar. In addition, the static frequency development speeds of trains. contents are clearly shown with strain measurements only. The low frequencies are 0.781, 0.977, 1.172, The Matlab Spectrogram toolbox is used to extract the three dimensional time-frequency maps 1.172 Hz and 0.879, 1.074, 1.172, 1.172 Hz of the opposite and passage directions for the 290, 360, 400, for the passage train at the mid-span point of the bridge at speeds 290 and 406 km/h, as shown in 406 km/h train Figurespeeds, 14. The re rsults sho espectively w that . It the po means wer that spectrum the strain density measur (PSD) at 2 ements 90 km/har speed e enough is lower to thestimate an the the PSD amplitude at 406 km/h. The PSD frequency responses’ amplitude differences between trains static and dynamic behavior in frequency domain. Moreover, from the comparison between the ﬁrst passage and departure (load and unload) show small values at 406 km/h. Therefore, it is concluded mode contents of the measurements and the FEM calculations, it can be concluded that the bridge is that the dynamic behavior of the bridge at train speeds of 406 km/h is greater than the 290 km/h. safe under its current dynamic behavior with the development speeds of trains. However, it is concluded that the bridge is safe at a speed of 406 km/h, but it should be continuously The Matlab monitored if Spectr trains speed ogram toolbox s are incre is ase used d above this to extract value. Moreov the three er, the dime incre nsional ase of P time-fr SD with train equency maps speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced for the passage train at the mid-span point of the bridge at speeds 290 and 406 km/h, as shown in vibrations, and, therefore, may be not suitable for an increase in the speed of train traffic. Figure 14. The results show that the power spectrum density (PSD) at 290 km/h speed is lower than the PSD amplitude at 406 km/h. The PSD frequency responses’ amplitude differences between trains passage and departure (load and unload) show small values at 406 km/h. Therefore, it is concluded that the dynamic behavior of the bridge at train speeds of 406 km/h is greater than the 290 km/h. However, it is concluded that the bridge is safe at a speed of 406 km/h, but it should be continuously monitored if trains speeds are increased above this value. Moreover, the increase of PSD with train speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced vibrations, and, therefore, may be not suitable for an increase in the speed of train trafﬁc. Number of cycles Number of cycles Appl. Sci. 2016, 6, 24 11 of 13 Appl. Sci. 2016, 6, 24 11 of 13 Appl. Sci. 2016, 6, 24 11 of 13 10 10 290 290 1 2 2 360 360 10 10 10 400 400 290 0 1 406 406 0 360 360 10 10 400 400 0 406 406 -1 10 -2 -2 -1 10 -2 -4 -3 -2 10 10 10 -4 -3 10 -4 10 10 -6 -4 -5 -6 -6 -5 -8 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Frequency (HZ) Frequency (HZ) -6 -8 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 (a) (b) Frequency (HZ) Frequency (HZ) 4 4 10 10 (a) (b) 290 290 4 4 360 360 10 10 10 2 290 290 406 3 406 2 360 400 400 406 406 1 2 0 10 0 1 0 10 -2 -1 0 10 10 -2 10 -1 -2 10 10 -4 -2 -3 10 10 -4 -6 -4 -3 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Frequency (HZ) Frequency (HZ) -6 -4 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 (c) (d) Frequency (HZ) Frequency (HZ) (c) (d) Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite- Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite-acceleration; acceleration; (c) passage-strain; (d) opposite-strain. Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite- (c) passage-strain; (d) opposite-strain. acceleration; (c) passage-strain; (d) opposite-strain. (a) (b) (a) (b) Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and (b) 406 km/h. Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and (b) 406 km/h. Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and 4. Summary and Conclusions (b) 406 km/h. 4. Summary and Conclusions This paper aims to evaluate the measurements of a structural health monitoring system of the KayaThis p railway aper brid aims to evaluat ge in Korea wit e the measur h high-speed ements of trains. A a structural non-destr health moni uctive monittori oring ng system of the system using 4. Summary and Conclusions accelerometer Kaya railway brid s and ge strain sensors is de in Korea with high signed to -speed t monitor the perf rains. A non-dest orma ructive mon nce of the itobri ring dge under new system using development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are accelerometers and strain sensors is designed to monitor the performance of the bridge under new This paper aims to evaluate the measurements of a structural health monitoring system of the analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are Kaya railway bridge in Korea with high-speed trains. A non-destructive monitoring system using - The mathematical correlation models describing the overall structural behavior of the bridge can analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: accelerometers and strain sensors is designed to monitor the performance of the bridge under new - be obta The maithemati ned with the sup cal correlap tion models descri ort of the health moni bing th toring system. e overall structural behav ior of the bridge can development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are - The torsion be obtained awi l response th the sup sho port of ws a higher e the healffect th th moni an toring system. the vibration res ponse on the bridge deck. analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: - - The effective The torsional torsion test response sho should ws a higher e be assessed ffect th at the en an the v d points o ibration res f the bridg ponse on e. the bridge deck. - - The mean The effective value contro torsion test l ch should art for the be asse acceleration ssed at the en and st d points o rain can be f th ap e bridg plied for e. bridge monitoring - The mathematical correlation models describing the overall structural behavior of the bridge can and for the early warning of any abnormal behavior. - The mean value control chart for the acceleration and strain can be applied for bridge monitoring be obtained with the support of the health monitoring system. and for the early warning of any abnormal behavior. - The torsional response shows a higher effect than the vibration response on the bridge deck. - The effective torsion test should be assessed at the end points of the bridge. Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Appl. Sci. 2016, 6, 24 12 of 13 - The mean value control chart for the acceleration and strain can be applied for bridge monitoring and for the early warning of any abnormal behavior. - The dynamic factor calculation shows that the static behavior increases with train speed developments. - The statistical analysis of cubic and quadratic ﬁts shows that the cubic ﬁtting in monitoring strain is better to predict the dynamic behavior of the strain contents. - The comparison between the results of acceleration and strain dynamic contents shows that the dynamic evaluation of acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents can be used to decrease the monitoring cost. - The fatigue performance of the bridge deck satisﬁes the requirement of inﬁnite-fatigue-life design method, and the highest cycles occur in a close region of stress mean and amplitude. Therefore, the bridge-deck fatigue is safe under current trains’ speeds. - The frequency calculation of the acceleration and strain measurements shows that the strain measurements are enough to estimate the static and dynamic behaviorin frequency domain. - Comparing the ﬁrst mode contents of the measurements and the FEM calculations shows that the dynamic behavior of the bridge is safe with development speeds of trains. - The increase of PSD with train speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced vibrations, and, therefore, may be not suitable for increased speed of train trafﬁc. - Based on the vibration, torsion, fatigue and frequency contents of the bridge, it is concluded that the bridge is safe under the development speed with a recommendation not to increase the train speed because the torsion performance is critical at 406 km/h at the entrance and exit monitoring points. Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, information and communication technology (ICT), and Future Planning (Grant No. 2013R1A2A2A01068174). Author Contributions: The authors have contributed equally to this work. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Lee, J.; Kim, S.; Kwark, J.; Lee, P.; Yoon, T. Dynamic characteristics of high-speed railway steel bridges. Trans. Korean Soc. Noise Vib. Eng. 2007, 17, 632–637. (In Korean). 2. Xia, H.; de Roeck, G.; Zhang, N.; Maeck, J. Experimental analysis of a high-speed railway bridge under Thalys trains. J. Sound Vib. 2003, 268, 103–113. [CrossRef] 3. Ding, Y.; Sun, P.; Wang, G.; Song, Y.; Wu, L.; Yue, Q.; Li, A. Early-Warning Method of Train Running Safety of a High-Speed Railway Bridge Based on Transverse Vibration Monitoring. Shock Vib. 2015, 2015, 518689. [CrossRef] 4. 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This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/evaluation-of-high-speed-railway-bridges-based-on-a-nondestructive-Hu90AoS4ah

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Publisher: Multidisciplinary Digital Publishing Institute
Copyright: © 1996-2019 MDPI (Basel, Switzerland) unless otherwise stated
ISSN: 2076-3417
DOI: 10.3390/app6010024
Publisher site: See Article on Publisher Site

Abstract

applied sciences Article Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System 1 , 2 1 , 3 , 4 Mosbeh R. Kaloop , Jong Wan Hu * and Emad Elbeltagi Received: 14 October 2015; Accepted: 11 January 2016; Published: 18 January 2016 Academic Editor: César M. A. Vasques Department of Civil and Environmental Engineering, Incheon National University, Incheon 406-840, Korea; mosbeh.kaloop@gmail.com Public Works and Civil Engineering Department, Mansoura University, Mansoura 35516, Egypt Incheon Disaster Prevention Research Center, Incheon National University, Incheon 406-840, Korea Structural Engineering Department, Mansoura University, Mansoura 35516, Egypt; eelbelta@mans.edu.eg * Correspondence: jongp24@incheon.ac.kr; Tel.: +0082-32-835-8463 Abstract: Recently, trains’ velocities in Korea increased more than the speed used in the design of some bridges. Accordingly, this paper demonstrates the evaluation of a railway bridge due to high-speed trains’ movement. A nondestructive monitoring system is used to assess the bridge performance under train speeds of 290, 360, 400 and 406 km/h. This system is comprised of a wireless short-term acceleration system and strain monitoring sensors attached to the bridge girder. The results of the analytical methods in time and frequency domains are presented. The following conclusions are obtained: the cross-correlation models for accelerations and strain measurements are effective to predict the performance of the bridge; the static behavior is increased with train speed developments; and the vibration, torsion, fatigue and frequency contents analyses of the bridge show that the bridge is safe under applied trains’ speeds. Keywords: high-speed; bridge; strain; monitoring; prediction 1. Introduction Nowadays, transportation by high-speed trains is considered as one of the important transportation facilities in South Korea as well as in the world’s high income countries.Therefore, increasing of the trains’ speed is one of the main problems facing existing infrastructure facilities. In Korea, high speed trains startedin 1992 from Seoul to Busan, while the Korea Train eXpress (KTX) services were launched on 1 April 2004.The bridges and infrastructure of express trains have been developing continuously from 1970. Most high-speed railway bridges are designed based on 350 km/h velocity. Therefore,with the velocity increase, existing bridges should be redesigned and evaluated. The newly-completed train HEMU-430X is currently running at high speed over 400km/h in the transportation network of Korea. Therefore, this study aims to evaluate the existing composite steel Kaya Bridge of Seoul-Busan High-Speed Railway under the effect of the high speed train movement. The acceleration and strain measurements are used to evaluate the composite bridge under velocities between 290 to 406 km/h. Lee et al. [1] evaluated steel and pre-stressed concrete (PC) box girder bridgesunder high speed trains up to 289.3 km/h. From their study, they found that no noticeable differences of dynamic responses due to the different materials (steel or concrete) could be found. Xia et al. [2] evaluated the real observation for the multi-span PC of high-speed railway bridges in a time domain. In their study, they recommended the use of the results as a reference for the design of high-speed railway bridges. Ding et al. [3] used the long term acceleration measurements to evaluate high-speed railway steel bridges. More monitoring systems for the effect of high-speed railway trains on bridges can Appl. Sci. 2016, 6, 24; doi:10.3390/app6010024 www.mdpi.com/journal/applsci Appl. Sci. 2016, 6, 24 2 of 13 be found in [4–7]. In general, the main objective of the structural health monitoring (SHM) systems is collecting the observations or information to detect and assess bridge condition, damage, fatigue Appl. Sci. 2016, 6, 24 2 of 13 and performance for proper and timely maintenance intervention. In order to identify the modes of bridge characteristics, it is necessary to excite the structure in order to produce a response at each in [4–7]. In general, the main objective of the structural health monitoring (SHM) systems is collecting relevant mode. The loads and response of structures are parameters for the monitoring of the bridge the observations or information to detect and assess bridge condition, damage, fatigue and performance performanc under e for proper an current and d timely m future loadings aintenance conditions. intervention. In order to Typical SHM idimplementations entify the modes ofin bridge highway characteristics, it is necessary to excite the structure in order to produce a response at each relevant and steel bridges are summarized in [8,9]. In addition, for continuous health monitoring studies, mode. The loads and response of structures are parameters for the monitoring of the bridge the response monitoring technique is more suitable [10]. performance under current and future loadings conditions. Typical SHM implementations in The evaluation methodologies of high speed railway bridges are concluded in [11–13]. highway and steel bridges are summarized in [8,9]. In addition, for continuous health monitoring Sartos et al. [14] assessed the stress/strain levels, load distributions, and fatigue for four different studies, the response monitoring technique is more suitable [10]. bridges based on strain measurements, and they concluded that the system is effective in the The evaluation methodologies of high speed railway bridges are concluded in [11–13]. static performance analysis. Xia et al. [15] used asimulation model to evaluate vertical and lateral Sartos et al. [14] assessed the stress/strain levels, load distributions, and fatigue for four different bridge behavior under high-speed trains. The results of their study showed that the deﬂections and bridges based on strain measurements, and they concluded that the system is effective in the static accelerations of the bridge girder are in accordance with the safety and comfort standards of bridges and performance analysis. Xia et al. [15] used asimulation model to evaluate vertical and lateral bridge running beha train vior under high-speed trains vehicles. Ding et al. [3] pr . Th oposed e results of the parametric their study (polynomial showed that ﬁtting) the deflectio and nonparametric ns and accelerations of the bridge girder are in accordance with the safety and comfort standards of bridges (correlation models, mean value control, root mean square (RMS)) statistical methodology for the and running train vehicles. Ding et al. [3] proposed the parametric (polynomial fitting) and acceleration measurements to study the safety and early-warning of the bridge. From their study, nonparametric (correlation models, mean value control, root mean square (RMS)) statistical they found that the quadratic polynomial ﬁtting provides a good capability for detecting the abnormal methodology for the acceleration measurements to study the safety and early-warning of the bridge. changes of the transverse acceleration measurements. Furthermore, the correlation models describing From their study, they found that the quadratic polynomial fitting provides a good capability for the overall structural behavior of the bridge can be obtained with the support of the health monitoring detecting the abnormal changes of the transverse acceleration measurements. Furthermore, the system, which includes cross-correlation models for accelerations. Liu et al. [6] concluded that the correlation models describing the overall structural behavior of the bridge can be obtained with the numerical simulation gives a good relation between the predicted and the measured responses. support of the health monitoring system, which includes cross-correlation models for accelerations. Therefore, the statistical analysis can be used to detect fatigue, torsion and reliability of structures Liu et al. [6] concluded that the numerical simulation gives a good relation between the predicted and basedthe measured on strain and response displacement s. Therefore measur , the ements statistica [16 l an –18 alysi ]. Furthermor s can be used to de e, parametric tect fatigue, models torsion areaused nd to reliability of structures based on strain and displacement measurements [16–18]. Furthermore, detect the performance of structures based on acceleration and strain measurements [19,20]. The main parametric models are used to detect the performance of structures based on acceleration and strain advantage of these methods is the ability to use them to evaluate and detect structural movements measurements [19,20]. The main advantage of these methods is the ability to use them to evaluate and damage. and detect structural movements and damage. The proposed study aims to evaluate Kaya bridge performance using a nondestructive monitoring The proposed study aims to evaluate Kaya bridge performance using a nondestructive system designed to assess the existing bridge under high-speed train movement as well as investigating monitoring system designed to assess the existing bridge under high-speed train movement as well the bridge structural behavior based on a simple application of time series and frequency analyses for as investigating the bridge structural behavior based on a simple application of time series and the acceleration and strain responses. Finally, the effectiveness of the monitoring sensors in time and frequency analyses for the acceleration and strain responses. Finally, the effectiveness of the frequency domains is assessed in order to decrease the monitoring system cost. monitoring sensors in time and frequency domains is assessed in order to decrease the monitoring system cost. 2. Kaya Bridge, High-Speed Trainsand Monitoring System Descriptions 2. Kaya Bridge, High-Speed Trainsand Monitoring System Descriptions Kaya Bridge, shown in Figure 1a, is a composite steel box girder bridge with 50 m span supports Kaya Bridge, shown in Figure 1a, is a composite steel box girder bridge with 50 m span supports two line high-speed railways. The cross-section of the bridge is shown in Figure 1b. Two longitudinal two line high-speed railways. The cross-section of the bridge is shown in Figure 1b. Two longitudinal girders are used with a spacing of 6.5 m and reinforced concrete deck with 14.00 m wide, which is used girders are used with a spacing of 6.5 m and reinforced concrete deck with 14.00 m wide, which is to provide the large stiffness for such heavy live loads. The use of a steel box as the deck system is used to provide the large stiffness for such heavy live loads. The use of a steel box as the deck system another feature in the design of this bridge, which is adopted to reduce uneven deﬂection and torsion is another feature in the design of this bridge, which is adopted to reduce uneven deflection and of the deck. The bridge design criteria at the mid span are presented in Table 1. torsion of the deck. The bridge design criteria at the mid span are presented in Table 1. (a) Figure 1. Cont. Appl. Sci. 2016, 6, 24 3 of 13 Appl. Sci. 2016, 6, 24 3 of 13 Appl. Sci. 2016, 6, 24 3 of 13 (b) Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). (b) Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). Figure 1. Kaya bridge (a) view and (b) cross section (dimensions in mm). Table 1.Railway Bridge design criteria [5]. Table 1. Railway Bridge design criteria [5]. Table 1.Railway Bridge design criteria [5]. Review Factor Criteria Note Review VerticalFactor acceleration Criteria 4.9 m/s For concrete t Note rack Review Factor Criteria Note Displacement as safety 82 mm 2 Under 350 km/h Vertical acceleration 4.9 m/s 2 For concrete track Vertical acceleration 4.9 m/s For concrete track Displacement Displacement as a safety s comfort 822mm 2 mm Under Under 35 350 0 km/h km/h Displacement as safety 82 mm Under 350 km/h Displacement as comfort 22 mm Under 350 km/h Track twist 0.4 mm/m By dynamic analysis Displacement as comfort 22 mm Under 350 km/h Track twist 0.4 mm/m By dynamic analysis Track twist 0.4 mm/m By dynamic analysis The next-generation high-speed developed train (HEMU-430X) is intended to travel with a maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in The next-generation high-speed developed train (HEMU-430X) is intended to travel with The next-generation high-speed developed train (HEMU-430X) is intended to travel with a the passage track with four different speeds, i.e., 290, 360, 400, and 406 km/h. a maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in maximum speed of 430 km/h (Figure 2).The high-speed train is controlled to pass over the bridge in the passage the passa track ge tra with ck wifour th four dif differfent erent speeds, speeds, i.e. i.e. ,, 290, 290, 36 360, 0, 4 400, 00, and and 40 406 6 km km/h. /h. Figure 2. Axial spacing and loading of the high-speed train. Figure 2. Axial spacing and loading of the high-speed train. To monitor the beha Figure vior of 2. Axial the bri spacing dge as and per the loading passof ing o thef the high-speed high speed tr train.ains, a wireless SHM system was designed and installed on the Kaya Bridge shortly after it was opened to use the To monitor the behavior of the bridge as per the passing of the high speed trains, a wireless SHM HEMU-430X train, as shown in Figure 3. This on-line concise SHM system was designed with a To monitor the behavior of the bridge as per the passing of the high speed trains, a wireless system was designed and installed on the Kaya Bridge shortly after it was opened to use the minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and SHMHEMU- system 430 was X tra designed in, as shown i andninstalled Figure 3. Thi on the s onKaya -line concise Bridge SHM shortly system after was it de was sign opened ed with to a use strain gauge sensors (five sensors on each railway track)with sampling frequency of 100Hz were minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and the HEMU-430X train, as shown in Figure 3. This on-line concise SHM system was designed with installed on the main girders under each railway track in each direction with equal-spacing of 8.3 m. strain gauge sensors (five sensors on each railway track)with sampling frequency of 100Hz were a minimum of 20 sensors to monitor the key parameters. Moreover, a total of 20 accelerometer and The sensors were installed at the bottom flange of the main girders in the vertical direction to detect installed on the main girders under each railway track in each direction with equal-spacing of 8.3 m. strain gauge sensors (ﬁve sensors on each railway track) with sampling frequency of 100 Hz were and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire The sensors were installed at the bottom flange of the main girders in the vertical direction to detect installed system cons on the main ists of gir a ders set ofunder sensors each , data railway acquistrack ition, dat in each a tran dir smiss ection ion, with data equal-spacing management and of a 8.3 m. and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire structural evaluation mechanism. The primary purpose of the system is to monitor in-service The sensors were installed at the bottom ﬂange of the main girders in the vertical direction to detect system consists of a set of sensors, data acquisition, data transmission, data management and a performance of the bridge structure under high-speed trains, and to provide early warning of and monitor the vertical vibration and fatigue of the bridge, as illustrated in Figure 3. The entire system structural evaluation mechanism. The primary purpose of the system is to monitor in-service abnormal changes in in-service performance of the bridge. Herein, the structural parameters to be consists of a set of sensors, data acquisition, data transmission, data management and a structural performance of the bridge structure under high-speed trains, and to provide early warning of monitored in the Kaya Bridge were determined using the structural sensitivity analysis with the finite evaluation mechanism. The primary purpose of the system is to monitor in-service performance of abnormal changes in in-service performance of the bridge. Herein, the structural parameters to be element model under the designed train speeds action, as shown in Kim et al. [5].Other important monitored in the Kaya Bridge were determined using the structural sensitivity analysis with the finite the bridge structure under high-speed trains, and to provide early warning of abnormal changes in parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge element model under the designed train speeds action, as shown in Kim et al. [5].Other important in-service performance of the bridge. Herein, the structural parameters to be monitored in the Kaya were monitored and studied under different speeds of the passing high-speed trains. parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge Bridge were determined using the structural sensitivity analysis with the ﬁnite element model under were monitored and studied under different speeds of the passing high-speed trains. the designed train speeds action, as shown in Kim et al. [5]. Other important parameters including accelerations, strains, deformations and fatigue of the steel box girder bridge were monitored and studied under different speeds of the passing high-speed trains. Appl. Sci. 2016, 6, 24 4 of 13 Appl. Sci. 2016, 6, 24 4 of 13 Appl. Sci. 2016, 6, 24 4 of 13 Figure 3. Structural health monitoring (SHM) system locations and components. Figure 3. Structural health monitoring (SHM) system locations and components. 3. Evaluation of the Bridge Condition Using SHM Monitoring Data 3. Evaluation of the Bridge Condition Using SHM Monitoring Data Kim et al. [5] designed a 3-D finite element model (FEM) of the Kaya Bridge using ANSYS V10.0 Figure 3. Structural health monitoring (SHM) system locations and components. software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of Kim et al. [5] designed a 3-D ﬁnite element model (FEM) of the Kaya Bridge using ANSYS V10.0 different train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are 3. Evaluation of the Bridge Condition Using SHM Monitoring Data software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of different concluded as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are concluded bendi Kim ng) et al. an d 5.913 (1 [5] designed st torsi a 3-o D fin n) Hz ite e folr the ementf model irst, second (FEM) o and thi f ther Ka d modes, respecti ya Bridge using A velyN ; SYS (ii) the V10.0 as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd bending) maximum vertical acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of and 5.913 250 (1st and torsion) 280 km/Hz h, resp forec the tive ﬁrst, ly; (isecond ii) the m and aximthir um accel d modes, eration respecti and dvely; isplace (ii) ment the for t maximum he train vertical different train’s speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are 400 km/h speed are 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the concluded as follows: (i) the natural frequency modes of the bridge are 3.186 (1st bending), 3.689 (2nd acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed 250 and 280 km/h, bridge design criteria in Table 1 shows that the bridge is safe under applied loads. bending) and 5.913 (1st torsion) Hz for the first, second and third modes, respectively; (ii) the respectively; (iii) the maximum acceleration and displacement for the train 400 km/h speed are The current study utilizes the real monitoring data that is collected from the SHM system of maximum vertical acceleration and displacement are 3.5 m/s and 6 mm occurred with train speed 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the bridge design criteria Kaya Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, 250 and 280 km/h, respectively; (iii) the maximum acceleration and displacement for the train in Table 1 shows that the bridge is safe under applied loads. torsional and fatigue behavior of the steel deck as well as evaluating and comparing the frequency 400 km/h speed are 1.6 m/s and 3.8 mm, respectively. Therefore, comparing the FEM results and the The current study utilizes the real monitoring data that is collected from the SHM system of Kaya contents for the measurements of the monitoring sensors. bridge design criteria in Table 1 shows that the bridge is safe under applied loads. Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, torsional The current study utilizes the real monitoring data that is collected from the SHM system of 3.1. Evaluation of the Bridge Girder Vibration Behavior and fatigue behavior of the steel deck as well as evaluating and comparing the frequency contents for Kaya Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring the measurements of the monitoring sensors. torsional and fatigue behavior of the steel deck as well as evaluating and comparing the frequency points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, contents for the measurements of the monitoring sensors. respectively. It can be seen that the dt, (dt = t2 (leave time) − t1 (entrance time)) values are reported as 3.1. Evaluation of the Bridge Girder Vibration Behavior 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that the 3.1. Evaluation of the Bridge Girder Vibration Behavior Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring vibration time effect on the bridge decreased by 68.65% as the train speed increased from Figure 4a,b illustrate the typical vertical acceleration time histories of the passage monitoring 290 to 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, points of the girder measured from accelerometers 1 to 5 for the 290 km/h and 406 km/h speed, 290 km/h are approximately the same, while as the speed increased, the entrance and exit monitoring respectively. It can be seen that the dt, (dt = t (leave time) t (entrance time)) values are reported 2 1 respecti points are ex vely. It ca periencin n be seen tha g high vibr t the ation r dt, (dt esponse com = t2 (leave time) pared to the − t1 (entr mid sp ance time an point. )) values are reported as as 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that 5.9, 2.92, 1.9 and 1.85 s with the speeds 290, 360, 400 and 406 km/h, respectively. This means that the the vibration time effect on the bridge decreased by 68.65% as the train speed increased from 290 to vibration time effect on the bridge decreased by 68.65% as the train speed increased from 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of 290 km/h 290 to 406 km/h. In addition, it is observed that the vibrations of the monitored points at a speed of are approximately the same, while as the speed increased, the entrance and exit monitoring points are 290 km/h are approximately the same, while as the speed increased, the entrance and exit monitoring experiencing high vibration response compared to the mid span point. points are experiencing high vibration response compared to the mid span point. (a) (b) Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a)passage response points for 290 km/h; (b) passage response points for 406 km/h. (a) (b) Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a)passage Figure 4. Vertical acceleration time histories of the girder caused by a high-speed train. (a) passage response points for 290 km/h; (b) passage response points for 406 km/h. response points for 290 km/h; (b) passage response points for 406 km/h. Appl. Sci. 2016, 6, 24 5 of 13 Appl. Sci. 2016, 6, 24 5 of 13 Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is much Appl.smaller Sci. 2016, 6than , 24 that of 290 km/h. In addition, it is noticed that the vibration response 5 of 13 for the Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, much smaller than that of 290 km/h. In addition, it is noticed that the vibration response for the Figure 5 shows that the vertical acceleration of the mid-span point of the girder at 406 km/h is the acceleration responses for the passage and opposite sides are equal. These indicate that, although passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, much smaller than that of 290 km/h. In addition, it is noticed that the vibration response for the the structural layouts of the ﬁve monitoring points of the main girder are the same, there is a signiﬁcant the acceleration responses for the passage and opposite sides are equal. These indicate that, although passage way is higher than that of the opposite side at 400 and 406 km/h, while at 290 and 360 km/h, the structural layouts of the five monitoring points of the main girder are the same, there is a difference between the vertical vibration characteristics of two sides of the bridge due to the rail the acceleration responses for the passage and opposite sides are equal. These indicate that, although significant difference between the vertical vibration characteristics of two sides of the bridge due to irregularity in the vertical directions with different speeds. Thus, there is a need to monitor the vertical the structural layouts of the five monitoring points of the main girder are the same, there is a the rail irregularity in the vertical directions with different speeds. Thus, there is a need to monitor accelerations of the span in the long term so as to realize anomaly alarms for vibration behavior significant difference between the vertical vibration characteristics of two sides of the bridge due to the vertical accelerations of the span in the long term so as to realize anomaly alarms for vibration of the the rail main irre girg der ularity .In addition, in the vertical d the measur irections w ed vertical ith different speed acceleration s. Thus, ther is smaller e is a n than eed to mo the FEM nitor results behavior of the main girder.In addition, the measured vertical acceleration is smaller than the FEM the vertical accelerations of the span in the long term so as to realize anomaly alarms for vibration by 62% according to the design criteria of the bridge (as shown Table 1),which means that the real results by 62% according to the design criteria of the bridge (as shown Table 1),which means that the behavior of the main girder.In addition, the measured vertical acceleration is smaller than the FEM response is safe. Furthermore, the girder torsional behavior should be studied with new development real response is safe. Furthermore, the girder torsional behavior should be studied with new results by 62% according to the design criteria of the bridge (as shown Table 1),which means that the trains’ speeds. development trains’ speeds. real response is safe. Furthermore, the girder torsional behavior should be studied with new development trains’ speeds. 0.8 0.8 290 290 360 360 0.6 0.6 0.8 400 0.8 400 290 40 296 0 0.4 0.4 0.6 0.6 400 400 0.2 0.2 406 406 0.4 0.4 0.2 0.2 -0.2 -0.2 0 0 -0.4 -0.4 -0.2 -0.2 -0.6 -0.6 -0.4 -0.4 -0.8 -0.8 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -0.6 -0.6 Time (Sec.) Time (Sec.) -0.8 -0.8 (a) (b) 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) Figure 5. Vertical acceleration (a) ( time histories of the passage and opposite mid bspan girder point. ( ) a) Figure 5. Vertical acceleration time histories of the passage and opposite mid span girder point. passage response of mid span point; (b) opposite response of mid span point. Figure 5. Vertical acceleration time histories of the passage and opposite mid span girder point. (a) (a) passage response of mid span point; (b) opposite response of mid span point. passage response of mid span point; (b) opposite response of mid span point. Figure 6 illustrates the dynamic torsional behavior of the bridge girder based on vertical Figur accelerat e 6ion illustrates measurem the entsdynamic [21].The t torsional orsional bbehavior ehavior of t ofhthe e girde bridge r due gir to der train p based asson age vertical at Figure 6 illustrates the dynamic torsional behavior of the bridge girder based on vertical 406 km/h is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds acceleration measurements [21].The torsional behavior of the girder due to train passage at 406 km/h acceleration measurements [21].The torsional behavior of the girder due to train passage at is shown in Figure 6b. is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds is shown 406 km/h is shown in Figure 6a. While the torsional behavior of the mid span point at different speeds a −a in Figure 6b. is shown in Figure 6b. T= (1) a a pass opps a −a T (1) T= (1) where T, a ,a and l are the torsion, acceleration for the passage and opposite points and distances between sensors’ positions. where T, a , a and l are the torsion, acceleration for the passage and opposite points and distances pass opps where T, a ,a and l are the torsion, acceleration for the passage and opposite points and between sensors’ positions. distance0. s bet 5 ween sensors’ positions. 0.05 P1 -0.5 0.50 2 4 6 8 10 12 14 0.05 -0.05 0.1 0 2 4 6 8 10 12 14 P1 290 0.1 P2 -0.5 0 2 4 6 8 10 12 14 -0.1 -0.05 0.10 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 0.2 0.1 P2 -0.1 P3 0 2 4 6 8 10 12 14 0 360 -0.1 0 0.2 0 2 4 6 8 10 12 14 -0.2 0.2 400 0 2 4 6 8 10 12 14 -0.0 1 0.2 P3 0 2 4 6 8 10 12 14 P4 0.2 -0.2 -0.2 0 2 4 6 8 10 12 40014 0 2 4 6 8 10 12 14 -0.2 0.2 0.20 2 4 6 8 10 12 14 P4 406 -0.0 2 P5 0 0 2 4 6 8 10 12 14 -0.2 0.2 0 2 4 6 8 10 12 14 -0.2 -1 10 2 4 6 8 10 12 14 0 2 4 6 8 10 12 40614 Time (sec) Time (sec) P5 -1 -0.2 (a) (b) 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (sec) Time (sec) Figure 6. Torsional measurements fo (a) ( r the bridge girder. (a)the torsional of the monitorin b) g points at 406 km/h passage; (b)mid span point torsional values. Figure 6. Torsional measurements for the bridge girder. (a)the torsional of the monitoring points at Figure 6. Torsional measurements for the bridge girder. (a) the torsional of the monitoring points at 406 km/h passage; (b)mid span point torsional values. It is noticed that the torsional values increased as the train velocity increased with maximum 406 km/h passage; (b) mid span point torsional values. values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased It is noticed that the torsional values increased as the train velocity increased with maximum values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased It is noticed that the torsional values increased as the train velocity increased with maximum values at points 1 and 5. As the speed increased from 290 to 406 km/h, the torsional values increased by 2 -2 2 -2 Torsional (rad/s ) Acc (ms ) Torsional (rad/s ) Acc (ms ) 2 -2 -2 Torsio 2nal (rad/s ) Acc (ms ) Torsional (rad/s ) Acc (ms ) Appl. Sci. 2016, 6, 24 6 of 13 61.20%, while they increased by 22.33% as the train speed increased from 360 to 406 km/h. This means Appl. Sci. 2016, 6, 24 6 of 13 that the bridge deck torsional response is higher than the vibration response. The girder torsional value at the mid span point, 0.103 rad/s , is within the design values, as shown in Table 1. by 61.20%, while they increased by 22.33% as the train speed increased from 360 to 406 km/h. This The statistical parameters, maximum and root mean square (RMS), for the acceleration means that the bridge deck torsional response is higher than the vibration response. The girder measur toements rsional vand alue at torsional the mid calculations span point, 0. at 10360 3 rad/ and s , 406 is wit km/h hin thar e de e pr sign esented valuesin , asT shown able 2.in T The able 1 360 km/h . The statistical parameters, maximum and root mean square (RMS), for the acceleration is assumed as the base-speed for the study of the bridge safety because the bridge design speed is measurements and torsional calculations at 360 and 406 km/h are presented in Table 2. The 360 km/h 350 km/h. Furthermore, the Bessel ﬁlter cut-off frequency of 30 Hz is applied to remove the noise is assumed as the base-speed for the study of the bridge safety because the bridge design speed is measurements of the accelerometer [21]. It is noticed that the maximum acceleration occurred at points 350 km/h. Furthermore, the Bessel filter cut-off frequency of 30 Hz is applied to remove the noise 1 and 5 for the passage and opposite directions at 406 km/h speed, respectively, with values within the measurements of the accelerometer [21]. It is noticed that the maximum acceleration occurred at design limits as shown in Table 1. While there is no high relative change at points 2 to 4, the RMS of points 1 and 5 for the passage and opposite directions at 406 km/h speed, respectively, with values the 360 km/h is smaller than that of the 406 km/h on passage way, while vice versa in the opposite within the design limits as shown in Table 1. While there is no high relative change at points 2 to 4, way except for point 5. The maximum torsion occurred at point 5 at a speed of 406 km/h with a value the RMS of the 360 km/h is smaller than that of the 406 km/h on passage way, while vice versa in the very close to the design value (Table 1). Thus, it is recommended to limit the train speed to 400 km/h opposite way except for point 5. The maximum torsion occurred at point 5 at a speed of 406 km/h only. In with addition, a value very cl the effective ose to the design v torsion test alue should (Tablebe 1). assessed Thus, it is rec at the ommended t end points o limit of the the tr bridge ain speed (point of to 400 km/h only. In addition, the effective torsion test should be assessed at the end points of the maximum torsion). bridge (point of maximum torsion). Table 2. Maximum acceleration, root mean square (RMS) and torsional acceleration for the ﬁltration of Table 2. Maximum acceleration, root mean square (RMS) and torsional acceleration for the filtration vibration measurements. of vibration measurements. 2 2 2 2 Acceleration (m/s ) RMS (m/s ) Max Torsion Acceleration (m/s)RMS(m/s ) Max (rad/s ) Passage Opposite Passage Opposite Torsion Point Point Passage Opposite Passage Opposite (rad/s ) 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 360 406 1 0.104 1.251 0.196 0.079 0.006 0.035 0.008 0.004 0.059 0.191 1 0.104 1.251 0.196 0.079 0.006 0.035 0.008 0.004 0.059 0.191 2 0.169 0.183 0.231 0.088 0.008 0.009 0.011 0.006 0.072 0.191 3 0.244 0.319 0.279 0.146 0.010 0.012 0.013 0.007 0.079 0.037 2 0.169 0.183 0.231 0.088 0.008 0.009 0.011 0.006 0.072 0.191 4 0.569 0.622 0.252 0.239 0.014 0.016 0.012 0.008 0.244 0.055 3 0.244 0.319 0.279 0.146 0.010 0.012 0.013 0.007 0.079 0.037 5 - 0.401 0.169 2.497 - 0.013 0.008 0.035 - 0.343 4 0.569 0.622 0.252 0.239 0.014 0.016 0.012 0.008 0.244 0.055 5 - 0.401 0.169 2.497 - 0.013 0.008 0.035 - 0.343 Ding et al. [3,17] concluded that the quadratic linear ﬁtting for the maximum and RMS are Ding et al. [3,17] concluded that the quadratic linear fitting for the maximum and RMS are calculated for the acceleration measurements can be used to detect the performance of high-speed calculated for the acceleration measurements can be used to detect the performance of high-speed railway bridges based on one span monitoring. Herein, this method is applied to detect and check railway bridges based on one span monitoring. Herein, this method is applied to detect and check the performance of the bridge due to different speeds on the two ways of the track. The maximum the performance of the bridge due to different speeds on the two ways of the track. The maximum and RMS of 150 acceleration of the original measurements are shown in Figure 7. The quadratic and RMS of 150 acceleration of the original measurements are shown in Figure 7. The quadratic linear linear ﬁtting is suitable for the maximum acceleration (Figure 7a), while the RMS shows no correlation fitting is suitable for the maximum acceleration (Figure 7a), while the RMS shows no correlation between the passage and opposite ways. Therefore, the maximum acceleration ﬁtting can be used to between the passage and opposite ways. Therefore, the maximum acceleration fitting can be used to check the safety of the bridge. check the safety of the bridge. 0.6 -3 x 10 y = - 0.34*x + 1.2*x - 0.001 0.5 0.4 0.3 0.2 0.1 -0.1 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0 1 2 3 4 5 6 7 8 -2 -2 -3 Acc Max. of Pass (ms ) RMS of Acc Pass (ms ) x 10 (a) (b) Figure 7. Cross-correlation between the passage and opposite way responses for 360 km/h. (a) Figure 7. Cross-correlation between the passage and opposite way responses for 360 km/h. cross-correlation maximum acceleration; (b) cross-correlation roote mean square (RMS) acceleration. (a) cross-correlation maximum acceleration; (b) cross-correlation roote mean square (RMS) acceleration. -2 Acc Max. of Opp (ms ) -2 RMS of Acc Opp (ms ) Appl. Sci. 2016, 6, 24 7 of 13 In this case, the ﬁtting equation is used to predict the opposite way measurements for the 400 and 406 km/h, as shown in Figure 8. The correlation coefﬁcient of the measured maximum values and Appl. Sci. 2016, 6, 24 7 of 13 predicted values with 400 and 406 km/h is 0.999 and 0.998 for opposite accelerations, respectively. In this case, the fitting equation is used to predict the opposite way measurements for the 400 The results indicate that good cross-correlation exists between the maximum values of the opposite and 406 km/h, as shown in Figure 8. The correlation coefficient of the measured maximum values way at the two train speeds. Herein, the control chart can be used to monitor the changes in the and predicted values with 400 and 406 km/h is 0.999 and 0.998 for opposite accelerations, respectively. vertical accelerations caused by deterioration of the vibration behavior. Firstly, the condition index The results indicate that good cross-correlation exists between the maximum values of the opposite e (e = Max (measurements) Max (prediction)) for early warning of abnormal vibration behavior is way at the two train speeds. Herein, the control chart can be used to monitor the changes in the deﬁned vert as ica the l accel differ erat ence ions c between aused by det the e measur rioration o ed fand the vibrat predicted ion beh maximum avior. Firstlvalues y, the condit of the ion accelerations index e in (e = Max (measurements) − Max (prediction)) for early warning of abnormal vibration behavior is the opposite way. Then, a mean value control chart is employed to monitor the time series of e with defined as the difference between the measured and predicted maximum values of the accelerations regard to the opposite accelerations. For online monitoring, the controlling parameter is chosen so in the opposite way. Then, a mean value control chart is employed to monitor the time series of e with that, when the structural vibration is in good condition, all observation samples fall between the regard to the opposite accelerations. For online monitoring, the controlling parameter is chosen so control limits. When the new measurement is made, the structural abnormal vibration condition can that, when the structural vibration is in good condition, all observation samples fall between the be detected if an unusual number of samples fall beyond the control limits. In this study, the control control limits. When the new measurement is made, the structural abnormal vibration condition can condition be det is assumed ected if an with unusu 290 al nu km/h mber o found f sampwithin les fall beyo 0.025 nd t to he cont 0.03 rom/s l limit.s.The In th calculated is study, the condition control index condition is assumed with 290 km/h found within 0.025 to −0.03 m/s . The calculated condition index for the 400 and 406 km/h is between 0.0043 and 0.028 m/s . Hence, a long-term monitoring of the for the 400 and 406 km/h is between 0.0043 and −0.028 m/s . Hence, a long-term monitoring of the maximum values of the opposite accelerations can help in the early-warning of the vibration behavior maximum values of the opposite accelerations can help in the early-warning of the vibration behavior deterioration. These results are identical with the Ding et al. [3,17] conclusions. deterioration. These results are identical with the Ding et al. [3,17] conclusions. (a) (b) Figure 8. Predicting the effect of cross-correlation between maximum values of accelerations by using Figure 8. Predicting the effect of cross-correlation between maximum values of accelerations by using quadratic polynomials. (a) 400 km/h; (b) 406 km/h. quadratic polynomials. (a) 400 km/h; (b) 406 km/h. 3.2. Evaluation of the Bridge Girders’ Train Responses 3.2. Evaluation of the Bridge Girders’ Train Responses The measured vertical strain histories of the monitoring points of the passage way for the train speeds 290 and 406 km/h are presented in Figure 9a,b. The vertical strain of the mid-span point of The measured vertical strain histories of the monitoring points of the passage way for the train passage and opposite ways for different train speeds are compared in Figure 9c,d. As the train speeds speeds 290 and 406 km/h are presented in Figure 9a,b. The vertical strain of the mid-span point of increase to 290, 360, 400 and 406 km/h, the strain responses (dt) decrease to 2.1, 1.93, 1.64 and 1.61 s, passage and opposite ways for different train speeds are compared in Figure 9c,d. As the train speeds respectively. This shows that the time of static strain responses decreases by 23.33% when the speed increase change to 290, s fr360, om 29 400 0 and and 40406 6 km km/h, /h. In add theitstrain ion, the r m esponses aximum s (dt train r ) decr esp ease onse to in t 2.1, he p 1.93, assa1.64 ge and and 1.61 s, opposite ways occurred at a speed of 290 km/h. The results as such show that the static and dynamic respectively. This shows that the time of static strain responses decreases by 23.33% when the speed behavior of the bridge is higher with low train speeds. In addition, the strain measurements of the changes from 290 and 406 km/h. In addition, the maximum strain response in the passage and opposite bridge points are highly correlated (0.95) with each speed change. This means that the strain ways occurred at a speed of 290 km/h. The results as such show that the static and dynamic behavior measurement of the mid span point can be used to detect the performance of the whole bridge. This of the bridge is higher with low train speeds. In addition, the strain measurements of the bridge points situation will decrease the cost of the monitoring system due to the use of one monitoring point only. are highly correlated (0.95) with each speed change. This means that the strain measurement of the mid The high correlation (0.99) of strain response for the passage and opposite ways occurred at 400 and 406 km/h. It means that the strain response of the two speeds is approximately equal in the static span point can be used to detect the performance of the whole bridge. This situation will decrease the response. However, to show clearly the relationship between dynamic and static response of the cost of the monitoring system due to the use of one monitoring point only. The high correlation (0.99) bridge, the dynamic increment factor should be calculated and analyzed. The Savitzky-Golay finite of strain response for the passage and opposite ways occurred at 400 and 406 km/h. It means that impulse response(FIR) smoothing filter is applied to detect the static strain of the bridge. The first the strain response of the two speeds is approximately equal in the static response. However, to show polynomial order with 101 frame size is utilized in this study. Figure 10a shows the strain clearly the relationship between dynamic and static response of the bridge, the dynamic increment measurements and filter data of the mid span of passage direction with a train speed of 406 km/h. factor should be calculated and analyzed. The Savitzky-Golay ﬁnite impulse response(FIR) smoothing Therefore, the dynamic increment factor (DF) can be calculated as follows [22,23]: ﬁlter is applied to detect the static strain of the bridge. The ﬁrst polynomial order with 101 frame size is utilized in this study. Figure 10a shows the strain measurements and ﬁlter data of the mid span of Appl. Sci. 2016, 6, 24 8 of 13 passage direction with a train speed of 406 km/h. Therefore, the dynamic increment factor (DF) can be Appl. Sci. 2016, 6, 24 8 of 13 calculated as follows [22,23]: dyn DF 1 A (2) DF = 1+ A (2) stc where A and A are the maximum absolute of dynamic and static amplitude of the strain, as shown Appl. Sci. 2016, 6, 24 8 of 13 dyn stc where A and A are the maximum absolute of dynamic and static amplitude of the strain, as in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the monitored points shown in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the are illustrated in Figure 10b. DF = 1+ (2) monitored points are illustrated in Figure 10b. where A 15 and A are the maximum abso lute of dynamic and st 5 atic amplitude of the strain, as shown in Figure 10a. The dynamic factors of passage (P) and opposite (O) directions for the monitored points are illustrated in Figure 10b. 0 -5 15 5 -5 -10 -10 -15 -15 P1 P1 P2 0 -5 P2 -20 P3 P3 -20 P4 -5 P4 -25 P5 -10 P5 -10 -30 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -15 -15 Time (Sec.) Time (Sec.) P1 P1 P2 P2 -20 P3 (a) (b) P3 -20 P4 P4 -25 P5 P5 10 15 -30 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) (a) (b) 10 15 -5 -5 -10 -10 -15 0 -15 290 -5 290 -5 -20 400 -20 -10 -10 -25 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 -15 -15 Time (Sec.) 290 Time (Sec.) -20 400 400 -20 (c) (d) 406 -25 -25 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Time (Sec.) Time (Sec.) Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response (c) (d) points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; (d) opposite response mid span points. Figure 9. Strain measurements of the main girder induced by high-speed train. (a) passing response (d) opposite response mid span points. points for 290 km/h; (b) passing response points for 406 km/h; (c) passage response mid span points; The dynamic factor calculation shows that the DF of 290 km/h train is higher than other train (d) opposite response mid span points. speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher The dynamic factor calculation shows that the DF of 290 km/h train is higher than other train than the passa The dynamic ge di factor rectio calculation n with all tr shows t ain speeds hat the except point (P DF of 290 km/h trai 3) with speeds o n is higher t f 400 an han other trai d 406 km n /h. speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher speeds at the passage and opposite directions. Furthermore, the DF of the opposite direction is higher than the passage direction with all train speeds except point (P3) with speeds of 400 and 406 km/h. than the passage direction with all train speeds except point (P3) with speeds of 400 and 406 km/h. (a) (b) (a) (b) Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and filtered static strain; (b) dynamic factor of the monitoring points. Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and filtered static Figure 10. Measured static strain and dynamic factor of the strain. (a) measured and ﬁltered static strain; (b) dynamic factor of the monitoring points. strain; (b) dynamic factor of the monitoring points. From Figure 10b, it can be seen that the DFs for the development speeds are less than two. It means From that F th igu e dom re 10b inant , it c p an e b rform e seen ance th o at t f thh ee DFs brid for ge the is stdevelopment speed atic with speeds ofs 36 are 0, 4 less th 00 and an two. It 406 km/h at means that the dominant performance of the bridge is static with speeds of 360, 400 and 406 km/h at all monitoring points, while the dynamic performance occurred at the opposite monitoring direction From Figure 10b, it can be seen that the DFs for the development speeds are less than two. It means all monitoring points, while the dynamic performance occurred at the opposite monitoring direction with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the that the dominant performance of the bridge is static with speeds of 360, 400 and 406 km/h at all with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the strain ( s) Strain (μs) strain ( s) Strain (μs) Strain (μs) strain (μs) Strain (μs) strain (μs) Appl. Sci. 2016, 6, 24 9 of 13 monitoring points, while the dynamic performance occurred at the opposite monitoring direction with train speed 290 km/h and point (O1) with train speed 400 km/h. These results indicate that the static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior should be studied to investigate the safety of the bridge under high train speed effect. Appl. Sci. 2016, 6, 24 9 of 13 Herein, the cross-correlation evaluation is used to predict the dynamic behavior of strain static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior contents. The same conditions used in the acceleration analysis are used in this part. Figure 11 should be studied to investigate the safety of the bridge under high train speed effect. presents the cross-correlation and the cubic ﬁtting of the maximum dynamic of strain measurements. Herein, the cross-correlation evaluation is used to predict the dynamic behavior of strain Appl. Sci. 2016, 6, 24 9 of 13 The relationship between the maximum dynamic of strain contents in the passage and opposite contents. The same conditions used in the acceleration analysis are used in this part. Figure 11 static behavior increases with increased train speeds. Therefore, the fatigue and frequency behavior presents the cross-correlation and the cubic fitting of the maximum dynamic of strain measurements. directions for the train speed of 360 km/h is illustrated in Figure 11a. While Figure 11b shows the should be The relation studship between ied to investigate the maxim the safetu ym dyn of the brid amic o gef unde strain r high conte train nts in speed the p effect. assage and opposite prediction of the opposite direction contents of the dynamic strain for 406 km/h, the statistical analysis direct Herei ionns, the cross- for the train correla speed tion eval of 360 km/ uatih on i is il s used lustra to predi ted in Figure ct the 11a. dyna Whm ile Fi ic beha gurevi 11b or of strain shows the of cubic and quadratic ﬁtting shows that the correlation coefﬁcient of quadratic is 0.90, so the cubic cont predi entsc. The tion of the opposi same conditions te di u rect sed io in n contents of the acceler the dyna ation analmi ysc is stra are in used for 406 in t km/h, the sta his part. Figurtei s11 tica l ﬁtting in presents the cross- this case is better correla to tion a predict nd the cubi the dynamic c fitting of th behavior e maximum of dyna the mstrain ic of stracontents. in measureme The nts.comparison analysis of cubic and quadratic fitting shows that the correlation coefficient of quadratic is 0.90, so The relationship between the maximum dynamic of strain contents in the passage and opposite the cubic fitting in this case is better to predict the dynamic behavior of the strain contents. The between the results of acceleration and strain dynamic contents shows that the dynamic evaluation of direct compa ionrsi for son b th ee tween the resul train speed oft 3 s of 60 a km/ ccelh e ra isti il on lust an ra d stra ted in Fi in dyna gure m 11 ic a. contents shows tha While Figure 11b t the dyna shows thmi e c acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents predi evac lua tion of the opposi tion of acceleratite di on isrect better ion contents of and effective to the dyna assess the vi mic stra brati in f oo n of the bri r 406 km/h, the sta dge, but the tisti stra cal in can be used to decrease the monitoring cost. andynam alysis of cubi ic contents can c and qua be used to dratic fitti dec ng shows tha rease the monito t the correla ring coti st. on coefficient of quadratic is 0.90, so the cubic fitting in this case is better to predict the dynamic behavior of the strain contents. The 7 18 comparison between the results of acceleration and strain dynamic contents shows that the dynamic 3 2 R=0.94 y = 0.1*x - 0.87*x + 2.9*x - 0.44 evaluation of acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents can be used to decrease the monitoring cost. 7 18 3 8 3 2 R=0.94 y = 0.1*x - 0.87*x + 2.9*x - 0.44 3 8 -1 -2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 1 2 3 4 5 6 7 8 Max. of Str. Pass (μs) Max Str. measured (μs) (a) (b) Figure 11. Cross-correlation and prediction of maximum strain dynamic. (a) cross-correlation Figure 11. -1 Cross-correlation and prediction of maximum -2 strain dynamic. (a) cross-correlation maxi 0 mu 0.5 m d 1 y 1.n 5ami 2 c of s 2.5 trai 3 n3. ; 5(b) 4predi 4.5 cting 5 effect of cross- 0correlation. 1 2 3 4 5 6 7 8 Max. of Str. Pass ( s) Max Str. measured ( s) μ μ maximum dynamic of strain; (b) predicting effect of cross-correlation. (a) (b) To perform fatigue evaluation, a simplified rain-flow cycle counting algorithm was used first to process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. Figure 11. Cross-correlation and prediction of maximum strain dynamic. (a) cross-correlation To perform fatigue evaluation, a simpliﬁed rain-ﬂow cycle counting algorithm was used ﬁrst to The spectra of stress matrix calculated using the strain history data with train speeds of 360 and maximum dynamic of strain; (b) predicting effect of cross-correlation. process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. 406 km/h for the passage (left) and opposite (right) as shown in Figure 12, respectively.In addition, the maximum stress c To perform fatigue eval ycles ar uation, e prese a simpli nted in F fied raiin gure -flow cycl 12. It is observed tha e counting algorithm wa t the ma s used ximum stress first to The spectra of stress matrix calculated using the strain history data with train speeds of 360 and process strain history data and the spectrum of stress matrix obtained by statistical analysis [17,24]. amplitude, obtained from strain history curves under two trains’ effects for the passage and opposite 406 km/h for the passage (left) and opposite (right) as shown in Figure 12, respectively. In addition, The spectra of stress matrix calculated using the strain history data with train speeds of 360 and directions, is smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher the maximum stress cycles are presented in Figure 12. It is observed that the maximum stress amplitude, 40stress 6 km/h rang for e. thMost cyc e passage les o (left ccur ) anin the re d opposit gion e (right of st) re as ss m shown in F ean and am igup re 1 litude 2, resp from ec t− iv 0.5 to 0.5 MPa ely.In addition, an d obtained from strain history curves under two trains’ effects for the passage and opposite directions, is the maximum stress c 0 to 0.5 MPa, respectiv yecles ar ly. Thus, e prese the m nted in F ean and iam gure pl 12 itude v . It is observed tha alues of the stress t the ma for the tximum stress wo trains in the amplitude, obtained from strain history curves under two trains’ effects for the passage and opposite two directions are equal. smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher stress range. directions, is smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher Most cycles occur in the region of stress mean and amplitude from 0.5 to 0.5 MPa and 0 to 0.5 MPa, stress range. Most cycles occur in the region of stress mean and amplitude from −0.5 to 0.5 MPa and respectively. Thus, the mean and amplitude values of the stress for the two trains in the two directions 0 to 0.5 MPa, respectively. Thus, the mean and amplitude values of the stress for the two trains in the are equal. 150 two directions are equal. X: 1.24e-005 Y: -0.06603 Z: 164 -2.5 100 2 -2 X: 1.24e-005 -1.5 1.5 Y: -0.06603 -1 Z: 164 -0.5 0.5 Mean (MPa) 0 Amplitude (MPa) -2.5 (a) -2 2 -1.5 1.5 -1 -0.5 0.5 Mean (MPa) Amplitude (MPa) (a) Figure 12. Cont. Number of cycles Max. of Str. Opp ( s) Number of cycles Max. of Str. Opp (μs) Max Str. predicted (μs) Max Str. predicted (μs) Appl. Sci. 2016, 6, 24 10 of 13 Appl. Sci. 2016, 6, 24 10 of 13 X: 3.828e-006 X: 4.496e-005 Y: -0.02062 Y : 0.4223 Z: 126.5 50 Z: 132 0 0 -2 -3 -1.5 2.5 1.2 -2 2 1 -1 0.8 -1 1.5 0.6 -0.5 0.4 0.5 0.2 Mean (MPa) Mean (MPa) Amplitude (MPa) Amplitude (MPa) (b) Figure 12. Rain-flow matrix of mid-span stress (a) 360 km/h; (b) 406 km/h. Figure 12. Rain-ﬂow matrix of mid-span stress (a) 360 km/h; (b) 406 km/h. The maximum number of stress cycles at 406 km/h is smaller than that occurring at 360 km/h in the two directions. The results show that the fatigue stress and number of cycles limit are 29 MPa and The maximum number of stress cycles at 406 km/h is smaller than that occurring at 360 km/h 2 × 10 , respectively [25], as recommended by Eurocode 3. The value of the equivalent stress in the two directions. The results show that the fatigue stress and number of cycles limit are 29 MPa amplitude and the number of cycles when the high-speed train passes through bridge is far less than and 2 10 thi,sr val espectively ue for two tra [25 ins. ],However, the fa as recommended tigue beha byvEur ior of ocode the bri3. dge deck The value satisfies the req of the equivalent uirement stress of the infinite-fatigue-life design method. amplitude and the number of cycles when the high-speed train passes through bridge is far less than this value for two trains. However, the fatigue behavior of the bridge deck satisﬁes the requirement of 3.3. Acceleration-Strain Frequency Domain Evaluation the inﬁnite-fatigue-life design method. The frequency contents of strain and acceleration measurements for the mid-span monitoring points in the passage and opposite directions are illustrated in Figure 13. The cross spectrum density 3.3. Acceleration-Strain Frequency Domain Evaluation function in Matlab (Version 7.6, MathWorks, Natics, MA, USA, 2008) is used to calculate the frequency contents.Based on the FEM [5] analysis, the band-pass filters in between 1 to 45 Hz with The frequency contents of strain and acceleration measurements for the mid-span monitoring 101 hamming window are used to filter the measured data to include the static and dynamic points in the passage and opposite directions are illustrated in Figure 13. The cross spectrum density frequency contents of the bridge. From Figure 13, the frequency contents are 3.223, 3.906 Hz and 3.223, function in4.Matl 199 Hz abfor (V29 ersion 0 and 7.6, 360 kMathW m/h at th orks, e oppo Natics, site and p MA, assage d USA, irect 2008) ions, re issp used ectiveto ly. In calculate additionthe , the frequency frequency contents equal (4.297 Hz) for the 400 and 406 km/h at the two directions. From the contents.Based on the FEM [5] analysis, the band-pass ﬁlters in between 1 to 45 Hz with 101 hamming comparison of the FEM frequency and real data, it is observed that the first dynamic mode changes window are used to ﬁlter the measured data to include the static and dynamic frequency contents of increased with increasing the trains’ speeds. The changes of passage frequency from the first bending the bridge. From Figure 13, the frequency contents are 3.223, 3.906 Hz and 3.223, 4.199 Hz for 290 and FEM frequency mode are 18.5%, 24.2%, 25.8% for the speeds 290, 360 and 406 km/h, respectively. The 360 km/h strain at the freque opposite ncies contents and passage at the two dir direction ections, s w riespectively th the effect of . In all tr addition, ains’ speeds are the frsimilar. In equency contents addition, the static frequency contents are clearly shown with strain measurements only. The low equal (4.297 Hz) for the 400 and 406 km/h at the two directions. From the comparison of the FEM frequencies are 0.781, 0.977, 1.172, 1.172 Hz and 0.879, 1.074, 1.172, 1.172 Hz of the opposite and frequency and real data, it is observed that the ﬁrst dynamic mode changes increased with increasing passage directions for the 290, 360, 400, 406 km/h train speeds, respectively. It means that the strain the trains’ speeds. The changes of passage frequency from the ﬁrst bending FEM frequency mode are measurements are enough to estimate the static and dynamic behavior in frequency domain. 18.5%, 24.2%, Moreover, f 25.8% for rom the compa the speeds rison between the fi 290, 360 and 406 rst km/h, mode contents respectively of the me . The asurements and strain frequencies the FEM contents calculations, it can be concluded that the bridge is safe under its current dynamic behavior with the at the two directions with the effect of all trains’ speeds are similar. In addition, the static frequency development speeds of trains. contents are clearly shown with strain measurements only. The low frequencies are 0.781, 0.977, 1.172, The Matlab Spectrogram toolbox is used to extract the three dimensional time-frequency maps 1.172 Hz and 0.879, 1.074, 1.172, 1.172 Hz of the opposite and passage directions for the 290, 360, 400, for the passage train at the mid-span point of the bridge at speeds 290 and 406 km/h, as shown in 406 km/h train Figurespeeds, 14. The re rsults sho espectively w that . It the po means wer that spectrum the strain density measur (PSD) at 2 ements 90 km/har speed e enough is lower to thestimate an the the PSD amplitude at 406 km/h. The PSD frequency responses’ amplitude differences between trains static and dynamic behavior in frequency domain. Moreover, from the comparison between the ﬁrst passage and departure (load and unload) show small values at 406 km/h. Therefore, it is concluded mode contents of the measurements and the FEM calculations, it can be concluded that the bridge is that the dynamic behavior of the bridge at train speeds of 406 km/h is greater than the 290 km/h. safe under its current dynamic behavior with the development speeds of trains. However, it is concluded that the bridge is safe at a speed of 406 km/h, but it should be continuously The Matlab monitored if Spectr trains speed ogram toolbox s are incre is ase used d above this to extract value. Moreov the three er, the dime incre nsional ase of P time-fr SD with train equency maps speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced for the passage train at the mid-span point of the bridge at speeds 290 and 406 km/h, as shown in vibrations, and, therefore, may be not suitable for an increase in the speed of train traffic. Figure 14. The results show that the power spectrum density (PSD) at 290 km/h speed is lower than the PSD amplitude at 406 km/h. The PSD frequency responses’ amplitude differences between trains passage and departure (load and unload) show small values at 406 km/h. Therefore, it is concluded that the dynamic behavior of the bridge at train speeds of 406 km/h is greater than the 290 km/h. However, it is concluded that the bridge is safe at a speed of 406 km/h, but it should be continuously monitored if trains speeds are increased above this value. Moreover, the increase of PSD with train speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced vibrations, and, therefore, may be not suitable for an increase in the speed of train trafﬁc. Number of cycles Number of cycles Appl. Sci. 2016, 6, 24 11 of 13 Appl. Sci. 2016, 6, 24 11 of 13 Appl. Sci. 2016, 6, 24 11 of 13 10 10 290 290 1 2 2 360 360 10 10 10 400 400 290 0 1 406 406 0 360 360 10 10 400 400 0 406 406 -1 10 -2 -2 -1 10 -2 -4 -3 -2 10 10 10 -4 -3 10 -4 10 10 -6 -4 -5 -6 -6 -5 -8 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Frequency (HZ) Frequency (HZ) -6 -8 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 (a) (b) Frequency (HZ) Frequency (HZ) 4 4 10 10 (a) (b) 290 290 4 4 360 360 10 10 10 2 290 290 406 3 406 2 360 400 400 406 406 1 2 0 10 0 1 0 10 -2 -1 0 10 10 -2 10 -1 -2 10 10 -4 -2 -3 10 10 -4 -6 -4 -3 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Frequency (HZ) Frequency (HZ) -6 -4 10 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 (c) (d) Frequency (HZ) Frequency (HZ) (c) (d) Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite- Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite-acceleration; acceleration; (c) passage-strain; (d) opposite-strain. Figure 13. Acceleration and strain frequency contents. (a) passage-acceleration; (b) opposite- (c) passage-strain; (d) opposite-strain. acceleration; (c) passage-strain; (d) opposite-strain. (a) (b) (a) (b) Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and (b) 406 km/h. Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and (b) 406 km/h. Figure 14. Time-frequency acceleration measurements for the trains speeds (a) 290 km/h and 4. Summary and Conclusions (b) 406 km/h. 4. Summary and Conclusions This paper aims to evaluate the measurements of a structural health monitoring system of the KayaThis p railway aper brid aims to evaluat ge in Korea wit e the measur h high-speed ements of trains. A a structural non-destr health moni uctive monittori oring ng system of the system using 4. Summary and Conclusions accelerometer Kaya railway brid s and ge strain sensors is de in Korea with high signed to -speed t monitor the perf rains. A non-dest orma ructive mon nce of the itobri ring dge under new system using development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are accelerometers and strain sensors is designed to monitor the performance of the bridge under new This paper aims to evaluate the measurements of a structural health monitoring system of the analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are Kaya railway bridge in Korea with high-speed trains. A non-destructive monitoring system using - The mathematical correlation models describing the overall structural behavior of the bridge can analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: accelerometers and strain sensors is designed to monitor the performance of the bridge under new - be obta The maithemati ned with the sup cal correlap tion models descri ort of the health moni bing th toring system. e overall structural behav ior of the bridge can development train speeds of 400 and 406 km/h. The static and dynamic behavior of the bridge are - The torsion be obtained awi l response th the sup sho port of ws a higher e the healffect th th moni an toring system. the vibration res ponse on the bridge deck. analyzed and discussed. Accordingly, the following remarks and conclusions are drawn: - - The effective The torsional torsion test response sho should ws a higher e be assessed ffect th at the en an the v d points o ibration res f the bridg ponse on e. the bridge deck. - - The mean The effective value contro torsion test l ch should art for the be asse acceleration ssed at the en and st d points o rain can be f th ap e bridg plied for e. bridge monitoring - The mathematical correlation models describing the overall structural behavior of the bridge can and for the early warning of any abnormal behavior. - The mean value control chart for the acceleration and strain can be applied for bridge monitoring be obtained with the support of the health monitoring system. and for the early warning of any abnormal behavior. - The torsional response shows a higher effect than the vibration response on the bridge deck. - The effective torsion test should be assessed at the end points of the bridge. Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Magnitude Appl. Sci. 2016, 6, 24 12 of 13 - The mean value control chart for the acceleration and strain can be applied for bridge monitoring and for the early warning of any abnormal behavior. - The dynamic factor calculation shows that the static behavior increases with train speed developments. - The statistical analysis of cubic and quadratic ﬁts shows that the cubic ﬁtting in monitoring strain is better to predict the dynamic behavior of the strain contents. - The comparison between the results of acceleration and strain dynamic contents shows that the dynamic evaluation of acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents can be used to decrease the monitoring cost. - The fatigue performance of the bridge deck satisﬁes the requirement of inﬁnite-fatigue-life design method, and the highest cycles occur in a close region of stress mean and amplitude. Therefore, the bridge-deck fatigue is safe under current trains’ speeds. - The frequency calculation of the acceleration and strain measurements shows that the strain measurements are enough to estimate the static and dynamic behaviorin frequency domain. - Comparing the ﬁrst mode contents of the measurements and the FEM calculations shows that the dynamic behavior of the bridge is safe with development speeds of trains. - The increase of PSD with train speeds indicates that the simple beam girders of steel bridges are very sensitive to train induced vibrations, and, therefore, may be not suitable for increased speed of train trafﬁc. - Based on the vibration, torsion, fatigue and frequency contents of the bridge, it is concluded that the bridge is safe under the development speed with a recommendation not to increase the train speed because the torsion performance is critical at 406 km/h at the entrance and exit monitoring points. Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, information and communication technology (ICT), and Future Planning (Grant No. 2013R1A2A2A01068174). Author Contributions: The authors have contributed equally to this work. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Lee, J.; Kim, S.; Kwark, J.; Lee, P.; Yoon, T. Dynamic characteristics of high-speed railway steel bridges. Trans. Korean Soc. Noise Vib. Eng. 2007, 17, 632–637. (In Korean). 2. Xia, H.; de Roeck, G.; Zhang, N.; Maeck, J. Experimental analysis of a high-speed railway bridge under Thalys trains. J. Sound Vib. 2003, 268, 103–113. [CrossRef] 3. Ding, Y.; Sun, P.; Wang, G.; Song, Y.; Wu, L.; Yue, Q.; Li, A. Early-Warning Method of Train Running Safety of a High-Speed Railway Bridge Based on Transverse Vibration Monitoring. Shock Vib. 2015, 2015, 518689. [CrossRef] 4. 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In Proceedings of the 7th International Symposium on Heavy Vehicles Weights and Dimensions, Delfet, The Netherland, 16–20 June 2002; pp. 289–302. 24. Li, S.; Li, H.; Liu, Y.; Lan, C.; Zhou, W.; Ou, J. SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge. Struct. Control Health Monit. 2014, 21, 156–172. [CrossRef] 25. European Committee for Standardization. Eurocode 3: Design of Steel Structures, Part 1–9: Fatigue; BS EN 1993-1-9:2005; European Committee for Standardization: Brussels, Belgium, 2005. © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

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Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System

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