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Patrick Smagt, Ben Krose (2018)
An introduction to neural networks
M. Losa, Renato Bacci, P. Leandri (2008)
A Statistical Model for Prediction of Critical Strains in Pavements from Deflection MeasurementsRoad Materials and Pavement Design, 9
(1991)
User’s Guide to MODCOMP3
M. Hossain, J. Zaniewski, S. Rajan (1994)
Estimation of Pavement‐Layer Moduli using Nonlinear Optimization TechniqueJournal of Transportation Engineering-asce, 120
R. Meier, G. Rix (1995)
BACKCALCULATION OF FLEXIBLE PAVEMENT MODULI FROM DYNAMIC DEFLECTION BASINS USING ARTIFICIAL NEURAL NETWORKSTransportation Research Record
(1993)
Layer Moduli Backcalculation Procedure: Software Selection. SHRP-P-651
M. Saltan, V. Uz, B. Aktaş (2013)
Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavementNeural Computing and Applications, 23
Maoyun Li, Hao Wang (2019)
Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parametersInternational Journal of Pavement Engineering, 20
K. Gopalakrishnan (2012)
Instantaneous pavement condition evaluation using non-destructive neuro-evolutionary approachStructure and Infrastructure Engineering, 8
M. Fakhri, A. Ghanizadeh (2014)
Modelling of 3D response pulse at the bottom of asphalt layer using a novel function and artificial neural networkInternational Journal of Pavement Engineering, 15
(2006)
Long-term pavement performance program manual for falling weight deflectometer measurements. United States. Federal Highway Administration. Office of Infrastructure Research and Development
Hossam El-Raof, R. El-Hakim, S. El-Badawy, H. Afify (2018)
Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer DataJournal of Transportation Engineering, Part B: Pavements
K. Chatti, M. Kutay, N. Lajnef, I. Zaabar, S. Varma, H. Lee (2017)
Enhanced Analysis of Falling Weight Deflectometer Data for Use With Mechanistic-Empirical Flexible Pavement Design and Analysis and Recommendations for Improvements to Falling Weight Deflectometers
Ali Maher, T. Bennert (2008)
Evaluation of Poisson’s Ratio for Use in the Mechanistic Empirical Pavement Design Guide (MEPDG)
K. Gopalakrishnan (2010)
Neural Network–Swarm Intelligence Hybrid Nonlinear Optimization Algorithm for Pavement Moduli Back-CalculationJournal of Transportation Engineering-asce, 136
Nune Rakesh, Ajai Jain, M. Reddy, K. Reddy (2006)
Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduliInternational Journal of Pavement Engineering, 7
S. Varma, M. Kutay (2016)
Backcalculation of viscoelastic and nonlinear flexible pavement layer properties from falling weight deflectionsInternational Journal of Pavement Engineering, 17
T. Fwa, T. Rani (2005)
Seed Modulus Generation Algorithm for Backcalculation of Flexible Pavement ModuliTransportation Research Record, 1905
F. Leiva-Villacorta, Adriana Vargas-Nordcbeck, D. Timm (2017)
Non-destructive evaluation of sustainable pavement technologies using artificial neural networksInternational journal of pavement research and technology, 10
G. Hayhoe (2002)
LEAF – A New Layered Elastic Computational Program for FAA Pavement Design and Evaluation Procedures
M. Shahin (2006)
Pavement Management for Airports, Roads, and Parking Lots
G. Scimemi, Tiziana Turetta, C. Celauro (2016)
Backcalculation of airport pavement moduli and thickness using the Lévy Ant Colony Optimization AlgorithmConstruction and Building Materials, 119
G. Garson (1991)
Interpreting neural-network connection weights, 6
A. Ghanizadeh, M. Fakhri (2014)
Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANNThe Scientific World Journal, 2014
K. Gopalakrishnan, M. Thompson (2004)
Backcalculation of Airport Flexible Pavement Non-Linear Moduli Using Artificial Neural Networks
D. Alexander, S. Kohn, W. Grogan (1989)
Nondestructive Testing Techniques and Evaluation Procedures for Airfield PavementsASTM special technical publications
Hossam Hassan, R. Mousa (2003)
Evaluation of nondestructive testing data using AASHTO and WESDEF backcalculation approachesJournal of Engineering and Applied Sciences, 50
M. Saltan, M. Tigdemir, M. Karasahin (2002)
Artificial Neural Network Application for Flexible Pavement Thickness ModelingTurkish Journal of Engineering and Environmental Sciences, 26
F. Cauwelaert, Alexander, T. White, Wr Barker (1989)
MULTILAYER ELASTIC PROGRAM FOR BACKCALCULATING LAYER MODULI IN PAVEMENT EVALUATIONASTM special technical publications
D. Brill, W. Hughes (2007)
New FAA Pavement Design SoftwareInternational Airport Review, 11
A. Ghanizadeh, A. Ziaie (2015)
NonPAS: A Program for Nonlinear Analysis of Flexible PavementsInternational Journal of Integrated Engineering, 7
H. Quintus, C. Rao, L. Irwin (2015)
Long-Term Pavement Performance Program Determination of In-Place Elastic Layer Modulus: Backcalculation Methodology and Procedures
(2002)
Backcalculation: an overview and perspective. In: Presented at the pavement evaluation conference
A. Goh (1995)
Back-propagation neural networks for modeling complex systemsArtif. Intell. Eng., 9
D. Gedafa, M. Hossain, Richard Miller, T. Van (2010)
Estimation of Remaining Service Life of Flexible Pavements from Surface Deflections
H. Ceylan, Alper Guclu, E. Tutumluer, M. Thompson (2005)
Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behaviorInternational Journal of Pavement Engineering, 6
J. Olden, Donald Jackson (2002)
Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networksEcological Modelling, 154
LTPP InfoPave Research quality pavement performance information
(2014)
Backcalculation of pavement layer properties using artificial neural network based gravitational search algorithm
C. Plati, P. Georgiou, V. Papavasiliou (2016)
Simulating pavement structural condition using artificial neural networksStructure and Infrastructure Engineering, 12
Sunil Sharma, A. Das (2008)
Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural networkCanadian Journal of Civil Engineering, 35
Hao Wang, Pengyu Xie, R. Ji, J. Gagnon (2020)
Prediction of airfield pavement responses from surface deflections: comparison between the traditional backcalculation approach and the ANN modelRoad Materials and Pavement Design, 22
A. Šarić, M. Pozder (2017)
Artificial Neural Networks Application in the Backcalculation Process of Flexible Pavement Layers Elasticity Modulus
D. Richardson, Michael Lusher (2015)
MoDOT pavement preservation research program volume III, development of pavement family and treatment performance models.
H. Quintus, Albert Bush, Gilbert Baladi (1989)
Nondestructive Testing of Pavements and Backcalculation of Moduli
A. Göktepe, E. Agar, A. Lav (2006)
Advances in backcalculating the mechanical properties of flexible pavementsAdv. Eng. Softw., 37
Y. Huang (1992)
Pavement analysis and design
M. Saltan, S. Terzi (2008)
Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduliAdv. Eng. Softw., 39
The primary objective of this research was to develop a model to accurately predict the modulus of flexible pavement layers from surface deflections measured using the falling weight deflectometer (FWD) device. To do so, a synthetic dataset consisting of 10,000 flexible pavements was created using the layered elastic theory. The developed dataset contained the moduli values for different pavement sections, and deflections at known distances from the load center. Next, the moduli of different asphalt pavement layers consisting of a surface course, base course, and subgrade were calculated using the Artificial Neural Network (ANN) methodology through backcalculation. The inputs for the neural network are thicknesses and deflection values at seven distances from the load center. The outputs are the moduli for different layers. The optimum neural network consists of two hidden layers and has a general architecture of 9-36-18-3. The transfer function is sigmoid for hidden layers and linear for the output layer. Results indicate that the ANN model can predict the modulus of different layers accurately with a coefficient of determination (R2) of more than 0.999 in all cases. For validation, the results from the developed model were compared with typical backcalculation software of ISSEM4, MODCOMP, MODULUS, WESDEF, and BAKFAA as well as 386 LTPP sections. Moreover, an analysis was conducted to assess the contribution of inputs in moduli prediction for each of the pavement layers using the Garson algorithm and the Connection Weight methods. It was concluded that the results from the latter method are in better agreement with the theories. Finally, the significance of different input variables was assessed using an indirect method by excluding each from the analysis and checking of the model predictability power. It was concluded that in order of significance, the base layer thickness, 1st geophone deflection, and the AC layer thickness are the variables that their exclusion causes large errors.
Innovative Infrastructure Solutions – Springer Journals
Published: Jun 18, 2020
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