Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors

Abstract

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.

Keywords

• fwd (falling weight deflectometer)
• asphalt concrete (ac) pavement
• neural network (nn)
• noise injection training
• nondestructive structural integrity