交通科学与工程2024,Vol.40Issue(2) :108-115.DOI:10.16544/j.cnki.cn43-1494/u.20230412240498

基于BP神经网络的固化红土抗压强度预测

Compressive strength prediction of solidified laterite based on BP neural network

王硕 唐正光 华伦
交通科学与工程2024,Vol.40Issue(2) :108-115.DOI:10.16544/j.cnki.cn43-1494/u.20230412240498

基于BP神经网络的固化红土抗压强度预测

Compressive strength prediction of solidified laterite based on BP neural network

王硕 1唐正光 1华伦1
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作者信息

  • 1. 昆明理工大学建筑工程学院,云南 昆明 650500
  • 折叠

摘要

为分析不同掺量的偏高岭土与石灰共同掺入玄武岩残积红土中对土体的改良效果,本试验选取偏高岭土的掺量分别为0%、2%、4%、6%和8%,石灰的掺量分别为0%、2.5%、5.0%、7.5%和10.0%,同时掺入玄武岩残积红土中,制作25组不同固化红土,对其进行28 d无侧限抗压强度正交试验,并用MATLAB软件建立神经网络预测模型,预测固化红土养护28 d的抗压强度.研究结果表明:本模型预测误差最大为4.56%,拟合度为0.997,且本方法比常规回归分析法更简单、更准确,可预测不同固结材料和掺量的固化红土抗压强度,提高试验效率.

Abstract

In order to explore the improvement effect of different dosages of metakaolin and lime mixed with residual soil of basaltic weathered soil,this experiment selected kaolin dosages of 0%,2%,4%,6%,and 8%,and lime dosages of 0%,2.5%,5.0%,7.5%,and 10.0%,simultaneously mixed with basalt residual laterite to prepare 25 groups of differently solidified red soils.An orthogonal test on the unconfined compressive strength of the soils was conducted for 28 days,and a neural network prediction model was established by MATLAB to predict the 28-day compressive strength of solidified laterite.The results show that the maximum prediction error of the model is 4.56%,with a coefficient of determination of 0.997.Furthermore,this method offers simplicity,higher efficiency,and greater accuracy over conventional regression analysis techniques,which enables the prediction of the compressive strength of solidified laterite with different consolidation materials and dosages,thereby improving experimental efficiency.

关键词

玄武岩残积红土/BP神经网络/抗压强度/强度预测模型/预测误差

Key words

basalt residual laterite/BP neural network/compressive strength/strength prediction model/prediction error

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基金项目

拉萨市设计院校企合作项目(649320200038)

出版年

2024
交通科学与工程
长沙理工大学

交通科学与工程

影响因子:0.444
ISSN:1674-599X
参考文献量19
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