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

机译：考虑建模误差的FWD测试数据评价沥青路面系统结构完整性

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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.
机译：本研究探讨了用于沥青路面系统的结构完整性评估技术，其考虑了材料不确定性引入的建模误差。为此，利用人工神经网络通过使用来自下降重量偏转仪测试的测量偏转数据来估计土层的弹性模量。基于频谱元件方法开发了一种多层路面系统的波分析程序，以便更准确和更快地计算。开发的程序应用于下降重量偏转器测试的数值模拟，具体用于可靠性分析和神经网络的训练和测试模式的产生。利用蒙特卡罗模拟集中研究了用于对给定路面系统进行建模的材料特性的影响，如泊松比和层厚度。结果表明，冲击载荷的幅度最显着，其次是层厚度和泊松比，在最大偏转比其他参数上更为显着。当输入数据被建模误差损坏时，还研究了神经网络的评估能力。发现由于建模错误，可以显着偏离估计结果。为了减少建模误差的效果，（提高算法的稳健性），我们提出了一种替代方案，以便考虑到任何建模错误来生成训练模式。然后，研究得出结论，通过使用来自广泛数值模拟研究的建议的培训模式，可以提高估计结果。

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