Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors
DOI:
https://doi.org/10.3846/bjrbe.2010.02Keywords:
FWD (Falling Weight Deflectometer), asphalt concrete (AC) pavement, neural network (NN), noise injection training, nondestructive structural integrityAbstract
This study examines the structural integrity assessment technique used for the asphalt pavement system that considers the modeling errors introduced by material uncertainties. To this end, the artificial neural network is utilized to estimate the elastic modulus of soil layers by using the measured deflection data from the Falling Weight Deflectometer test. A wave analysis program for a multi-layered pavement system is developed based on the spectral element method for more accurate and faster calculation. The developed program is applied for the numerical simulation of the Falling Weight Deflectometer tests, specifically for the reliability analysis and the generation of training and testing patterns for the neural network. The effects of uncertainties in the material properties for modeling a given pavement system such as Poisson ratio and layer thickness are intensively investigated using the Monte Carlo Simulation. Results reveal that the amplitude of impact loads is most significant, followed by the layer thickness and the Poisson ratio, which are more significant on the max deflections than other parameters. The evaluation capability of the neural network is also investigated when the input data is corrupted by the modeling errors. It is found that the estimation results can be significantly deviated due to the modeling errors. To reduce the effect of the modeling error, (to improve the robustness of the algorithm), we proposed an alternative scheme in order to generate the training patterns taking into consideration any modeling errors. The study then concludes that the estimation results can be improved by using the proposed training patterns from an extensive numerical simulation study.
References
Aavik, A.; Paabo, P.; Kaal, T. 2006. Assessment of Pavement Structural Strength by the falling Weight Deflectometer, The Baltic Journal of Road and Bridge Engineering 1(4): 193–199.
Al-Khoury, R.; Scarpas, A.; Kasbergen, C.; Blaauwendraad, J. 2001a. Spectral Element Technique for Efficient Parameter identification of layered Media. I Forward Calculation, International Journal of Solids and Structures 38(9): 1605–1623. doi:10.1016/S0020-7683(00)00112-8
Al-Khoury, R.; Scarpas, A.; Kasbergen, C.; Blaasuwendraad, J. 2001b. Spectral element technique for efficient parameter identification of layered media. II Inverse calculation, International Journal of Solids and Structures 38(48–49): 8753–8772. doi:10.1016/S0020-7683(01)00109-3
Bertulienė, L.; Laurinavičius, A. 2008. Research and Evaluation of Methods for Determining Deformation Modulus of Road Subgrade and Frost Blanket Course, The Baltic Journal of Road and Bridge Engineering 3(2): 71–76. doi:10.3846/1822-427X.2008.3.71-76
Choi, S. M.; Choi, J. S.; Kim, S. I. 2002. Dynamic Backcalucation of Asphalt Concrete Pavement Moduli Using Artificial Neural Network, KSCE Journal of Civil Engineering 22(6D): 1125–1135.
Dong, Q. X.; Hachiya, Y.; Takahashi, O.; Tsubokawa, Y.; Matsui, K. 2002. An Efficient Backcalculation Algorithm of Time Domain for Large-Scale Pavement Structures Using Ritz Vectors, Finite Elements in Analysis and Design 38(12): 1131–1150. doi:10.1016/S0168-874X(02)00055-0
Ghasemi, F. Sh. A.; Abrishamian, M. S. 2007. A Novel Method for FDTD Numerical GPR Imaging of Arbitrary Shapes Based on Fourier Transform, NDT&E International 40(2): 140–146. doi:10.1016/j.ndteint.2006.09.002
Jo, B. W.; Tae, G. H.; Kwon, O. H. 2003. The Study on Back Calculation of Pavement Moduli with Genetic Algorithms, KSCE Journal of Civil Engineering 23(3D): 319–328.
Kim, J. M.; Mun, S. H. 2008. Fast Spectral Analysis of an Axisymmetric Layered Structure, Mechanics Research Communications 35(4): 222–228. doi:10.1016/j.mechrescom.2008.02.002
Kim, Y. G.; Kim, Y. R. 1998. Layer Moduli Prediction of Asphalt Concrete Pavements Using Artificial Neural Network, KSCE Journal of Civil Engineering 2(4): 467–479.
Liang, R. Y.; Zhu, J. X. 1995. Dynamic Analysis of Infinite Beam on Modified Vlasov Subgrade, Journal of Transportation Engineering 121(5): 434–442. doi:10.1061/(ASCE)0733-947X(1995)121:5(434)
Loizos, A.; Plati, C. 2007. Accuracy of Pavement Thicknesses Estimation Using Different Ground Penetrating Radar Analysis Approaches, NDT&E International 40(2): 147–157. doi:10.1016/j.ndteint.2006.09.001
Matsuoka, K. 1992. Noise Injection into Inputs in Back-Propagation Learning, IEEE Transactions on Systems, Man, and Cybernetics 22(3): 436–440.
Vapnik, V. N.; Chervonenkis, A. Y. 1971. On the Uniform Convergence of Relative Frequencies of Events to their Probabilities, Theory of Probability and its Applications 16(2): 264–280. doi:10.1137/1116025
Yun, C.-B.; Bahng, E. Y. 2000. Substructural Identification Using Neural Networks, Computers & Structures 77(1): 41–52. doi:10.1016/S0045-7949(99)00199-6
Yun, C. B.; Kim, J. M.; Hyun, C. H. 1995. Axisymmetric Elastodynamic Infinite Elements for Multi-Layered Half-Space, International Journal for Numerical Methods in Engineering 38(22): 3723–3743. doi:10.1002/nme.1620382202
Downloads
Published
Issue
Section
License
Copyright (c) 2010 Vilnius Gediminas Technical University (VGTU) Press Technika
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
