The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.

中文翻译：

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评估人工神经网络性能以预测多层柔性路面的层间条件和层模量

本研究的目的是评估人工神经网络（ANN）方法的性能，该方法可预测多层柔性路面结构的层间条件和层模量。为了实现这一目标，提出了两个基于ANN的反算模型来预测路面结构的层间条件和层模量。相应的数据库是使用基于ANSYS的有限元方法计算构建的，用于四种类型的结构，它们承受着重量下降的挠度计载荷。此外，通过将ANN模型的结果与PADAL和双重多元回归模型的结果进行比较，验证了两个拟议的ANN模型。测得的路面挠度盆数据用于验证。比较结果表明，ANN估计的结果与双倍多元回归模型之间没有显着差异。由于路面结构不完全连续，因此无法反映真实的路面结构，因此PADAL建模结果不准确。预测和验证结果表明，利用人工神经网络开发的反向计算模型可用于准确预测层模量和层间条件。此外，反算模型通过考虑层间条件避免了反算误差，而在已发表的研究报告中，以前的模型几乎没有考虑到这一点。由于路面结构不完全连续，因此无法反映真实的路面结构，因此PADAL建模结果不准确。预测和验证结果表明，利用人工神经网络开发的反向计算模型可用于准确预测层模量和层间条件。此外，反算模型通过考虑层间条件避免了反算误差，而在已发表的研究报告中，以前的模型几乎没有考虑到这一点。由于路面结构不完全连续，因此无法反映真实的路面结构，因此PADAL建模结果不准确。预测和验证结果表明，利用人工神经网络开发的反向计算模型可用于准确预测层模量和层间条件。此外，反算模型通过考虑层间条件避免了反算误差，而在已发表的研究报告中，以前的模型几乎没有考虑到这一点。