基于优化BP神经网络的复合路基沉降预测  

Research on settlement prediction of composite subgrade based on optimized BP neural network

作　　者：张建 易文[2] 袁伟嘉 ZHANG Jian;YI Wen;YUAN Weijia(Comprehensive Administrative Law Enforcement Brigade of Guilin Sub-district Office in Tongnan District,Chongqing 402660,China;School of Civil Engineering,Central South University of Forestry and Technology,Changsha 410004,Hunan,China;Poly Changda Engineering Company Limited,Guangzhou 510000,Guangdong,China)

机构地区：[1]重庆市潼南区桂林街道办事处综合行政执法大队,重庆402660 [2]中南林业科技大学土木工程学院,湖南长沙410004 [3]保利长大工程有限公司,广东广州510000

出　　处：《工程建设》2024年第3期6-10,16,共6页Engineering Construction

基　　金：国家948资助项目(2015-4-38);湖南省交通科技计划资助项目(201803,201303)。

摘　　要：为准确预测CFG桩复合路基的沉降,以观测时间、累计填土厚度、软土层厚度、软土压缩模量和桩长为输入变量,基于MATLAB平台,构建网络结构为5-5-1的BP预测模型,并用粒子群算法和遗传算法分别进行优化,再以肇庆市桥北路新建工程的实测数据进行仿真,将两种优化模型和普通BP模型的预测性能进行对比。结果表明:使用PSO-BP和GA-BP预测模型预测CFG桩复合路基的沉降是可行的,且预测精度高,预测结果明显优于普通BP沉降预测模型。本文成果可为复合路基的沉降预测提供一定的借鉴与参考。In order to accurately predict the settlement of CFG pile composite subgrade,the observation time,cumulative fill thickness,soft soil thickness,soft soil compression modulus and pile length are taken as input variables,and a BP prediction model with a network structure of 5-5-1 is constructed based on the MATLAB platform.The BP prediction model is optimized by particle swarm optimization and genetic algorithm,and then the measured data of the new construction project of Qiaobei road in Zhaoqing city is simulated,finally the prediction performances of two kinds of optimization models are compared with the ordinary BP model.The results show that it is feasible to use the PSO-BP and GA-BP prediction models to predict the settlement of CFG pile composite subgrade,and the prediction accuracy is high,and the prediction results are significantly better than the ordinary BP settlement prediction models.The results can provide some references for the settlement prediction of composite subgrade.

关 键 词：神经网络 PSO-BP GA-BP CFG桩复合路基 沉降预测 

分 类 号：U416.1[交通运输工程—道路与铁道工程]