The repair method for pavements should be selected considering the structural capacity of sublayers, in addition to the conditions observed at the pavement surface, to reduce the recurrence of distress in the repaired area. However, it is practically impossible to include the structural capacity of sublayers in the database of the pavement management system (PMS) because this would require additional tests in all expressway sections. Therefore, an artificial neural network model for predicting the indirect tensile strength (ITS) of the intermediate layer of all asphalt pavement sections in an expressway was developed in this study, taking the international roughness index, rut depth, surface distress, and equivalent single axle load as independent variables. The ITS of specimens cored from target sections was measured in the laboratory, and the PMS data for the target sections were collected. The ITS was predicted by conducting a feedforward process prior to the training step. When the error between the predicted and measured ITSs exceeded the allowable error, the model was repetitively trained using the resilient backpropagation method until the error fell within the acceptable boundary. The model was validated by analyzing the correlations between the ITSs predicted from the data of the training and test sets. Finally, the model was complemented by the corresponding minimum and maximum values of the ITS measured at the target section.

除在人行道表面观察到的情况外，还应考虑到亚层的结构能力来选择人行道的修补方法，以减少在修补区域发生的麻烦。但是，实际上不可能在路面管理系统（PMS）的数据库中包含子层的结构容量，因为这将需要在所有高速公路路段中进行附加测试。因此，本研究利用国际粗糙度指数，车辙深度，表面遇险情况和等效单轴，建立了一个人工神经网络模型来预测高速公路所有沥青路面部分中间层的间接抗张强度（ITS）。加载为自变量。从目标切片取芯的标本的ITS在实验室中进行了测量，并收集目标区域的PMS数据。通过在训练步骤之前进行前馈过程来预测ITS。当预测的ITS和测量的ITS之间的误差超过允许误差时，使用弹性反向传播方法重复训练模型，直到误差落入可接受的范围内。通过分析从训练和测试集的数据预测的ITS之间的相关性来验证模型。最后，在目标部分测得的ITS的相应最小和最大值对模型进行了补充。使用弹性反向传播方法反复训练模型，直到误差落在可接受的范围内。通过分析从训练和测试集的数据预测的ITS之间的相关性来验证模型。最后，在目标部分测得的ITS的相应最小和最大值对模型进行了补充。使用弹性反向传播方法反复训练模型，直到误差落在可接受的范围内。通过分析从训练和测试集的数据预测的ITS之间的相关性来验证模型。最后，在目标部分测得的ITS的相应最小和最大值对模型进行了补充。