长江科学院院报2019，Vol.36Issue(4)：88-92,5.DOI:10.11988/ckyyb.20180281
应用GA-BP神经网络预估砾类土的最大干密度
Estimating Maximum Dry Density of Gravel Soil by Back Propagation Neural Network Optimized by Genetic Algorithm
摘要
Abstract
A model of estimating the maximum dry density of gravel soil is established to provide reference for con-trolling the compaction quality of gravel soil projects and selecting the gravel soil which meets engineering require-ments. 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 in-put 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 net-work 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关键词
砾类土/最大干密度/全级配/GA-BP神经网络/遗传算法Key words
gravel soil/ maximum dry density/ full gradation/ GA-BP neural network/ genetic algorithm分类
建筑与水利引用本文复制引用
饶云康,丁瑜,许文年,张亮,张恒,潘波..应用GA-BP神经网络预估砾类土的最大干密度[J].长江科学院院报,2019,36(4):88-92,5.基金项目
国家自然科学基金项目(51879246,51408439) （51879246,51408439）
铁道部科技研究开发资助项目(2010G003-F) （2010G003-F）
潍坊市科技发展计划资助项目(2017GX083) （2017GX083）
中国海洋大学青年英才工程计划项目(841717014) （841717014）
山东省职业教育技艺技能传承创新平台(公路工程智慧检测技术传承创新平台)资助项目 （公路工程智慧检测技术传承创新平台）
