应用GA-BP神经网络预估砾类土的最大干密度

建立砾类土最大干密度预估模型,为控制砾类土工程填筑压实质量、选取满足工程压实性能要求的砾类土提供最大干密度预估参考。颗粒级配是决定砾类土最大干密度的关键因素,收集并整理得到92组砾类土数据,以全级配(d₁₀～d₁₀₀)作为BP(GA-BP)神经网络的输入变量,利用遗传算法优化BP神经网络的初始权值与阀值,构建基于BP神经网络和遗传算法的砾类土最大干密度预估模型,并与BP神经网络进行对比。86组训练样本预估结果的平均相对误差为0.54%,决定系数为0.983;6组检测样本预估结果的平均相对误差为0.57%,证明该网络模型泛化性能良好。采用GA-BP神经网络,由全级配能较好地预估砾类土最大干密度,收敛速度、预估精度及泛化性能均优于标准的BP神经网络模型。

Estimating Maximum Dry Density of Gravel Soil by Back Propagation Neural Network Optimized by Genetic Algorithm

A model of estimating the maximum dry density of gravel soil is established to provide reference for controlling the compaction quality of gravel soil projects and selecting the gravel soil which meets engineering requirements. In the light that particle gradation is the crucial factor that determines the maximum dry density of gravel soil, 92 groups of data of gravel soil are collected and obtained, of which full gradation (d₁₀-d₁₀₀) is used as the input variable of back propagation (BP) neural network. Furthermore, genetic algorithm (GA) is adopted to optimize the initial weights and thresholds of the BP neural network, based on which the estimation model for maximum dry density of gravel soil is constructed. In addition, the GA-BP neural network model is compared with BP neural network model. According to estimation results, the mean relative error of the predicted results of 86 groups of training samples is 0.54%, and the coefficient of determination is 0.983; the mean relative error of the predicted results of 6 groups of test samples is 0.57%, which indicates that the proposed model is of good generalization performance. It is concluded that the maximum dry density of gravel soil could be well predicted by applying GA-BP neural network in consideration of full gradation. GA-BP neural network model is superior than conventional BP neural network model in terms of convergence rate, prediction accuracy and generalization performance.