交通荷载下煤矸石路基填料累积变形PSO-BP神经网络预测模型

基于煤矸石路基填料大型动三轴试验结果，采用灰色关联分析法分析累积变形影响因子，确定了围压、压实度、级配参数、循环荷载振动次数4个特征参数。引入PSO算法对BP神经网络的权重、阈值进行全局寻优并赋值，提出了一种煤矸石路基填料累积变形PSO-BP神经网络预测模型。与传统BP神经网络模型对比结果验证了该预测模型的可行性和优越性，并通过不同学习程度下模型的预测效果分析了模型的泛化能力，证明了模型的预测潜力。

PSO-BP neural network prediction model for cumulative deformation of coal gangue subgrade filler under traffic loading

Based on the results of large-scale dynamic triaxial tests on coal gangue subgrade fillers, the grey correlation analysis method was used to analyze the influencing factors of cumulative deformation, and four characteristic parameters were determined, including confining pressure, compactness, gradation, and cyclic loading number. The particle swarm optimization (PSO) algorithm was used to globally optimize and assign the weights and thresholds of the back propagation (BP) neural network, and a PSO-BP neural network prediction model for cumulative deformation of coal gangue subgrade fillers was established. By comparing the traditional BP neural network with the established prediction model, the feasibility and superiority of the established prediction model were verified. The generalization ability of the model was further analyzed by comparing the prediction effects of the model under different learning levels, which proved the prediction potential of the model.