Study on ?－SVR and modified GA in back analysis of initial stress fields from displacements

YU Zhi-xiong; ZHOU Chuang-bing; CHEN Yi-feng; LI Jun-ping

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Study on ?－SVR and modified GA in back analysis of initial stress fields from displacements

更新时间：2026-06-25

- Study on ?－SVR and modified GA in back analysis of initial stress fields from displacements
- Vol. 28, Issue 1, (2007)
- 作者机构：
  
  1. 武汉大学水资源与水电工程科学国家重点实验室,武汉,430072
  2. 广州市广园路建设公司,广州,510010
- 作者简介：
- 基金信息：
- DOI：
  CLC： O242
- Online First：28 August 2013，
  
  Published：2007
- 稿件说明：
移动端阅览
YU Zhi-xiong, ZHOU Chuang-bing, CHEN Yi-feng, et al. Study on ?－SVR and modified GA in back analysis of initial stress fields from displacements[J]. 2007, 28(1).
DOI：

YU Zhi-xiong, ZHOU Chuang-bing, CHEN Yi-feng, et al. Study on ?－SVR and modified GA in back analysis of initial stress fields from displacements[J]. 2007, 28(1). DOI：

摘要

基于一种改进的支持向量机（?－SVR）和改进的遗传算法（GA），提出一种初始地应力场位移反分析方法。该方法通过正交设计方法安排较少次数的正分析方案，用?－SVR对正分析中测点位移值与参数构成的样本集进行学习，建立参数取值与观测点测值的非线性隐式方程；采用确定性或随机反分析的思路确定反分析的优化目标函数，并利用GA搜索最优参数取值。采用?－SVR方法建立的参数取值与位移量测点测值的非线性隐式方程，能够以很高的精度拟合和预测不同参数取值时的观测点测值，因此，可以用该隐式方程代替正分析，减少计算量；采用改进的GA方法能够准确搜索到最优参数；搜索到的最优参数值与理论值相当吻合。算例表明，基于?－SVR和GA的初始地应力场位移反分析方法是一种行之有效的初始地应力场位移反分析方法，可以广泛用于初始地应力场确定性反分析和随机反分析。

Abstract

A method of back analysis of initial stress fields from displacement is presented based on a kind of modified support vector machine named ?-SVR and a kind of modified genetic algorithms. By using orthogonal experiment method

only several positive analysis are done. ?-SVR used the measuring points’ displacement and parameters of positive analysis as samples to build the nonlinear implicit equations between them. Then modified genetic algorithms are used to find the optimal parameters on deterministic or stochastic optimal object function. The equations built by ?-SVR can fit and forecast the measuring points’ displacements under different parameters with high precision. So these equations can substitute positive analysis

and the amount of computation will reduce greatly. With modified genetic algorithms

the optimal parameters can be found accurately. Example shows the method presented is an effective back analysis method for initial stress fields from displacements. It can be widely used in deterministic or stochastic back analysis of initial stress fields from displacements.

关键词

Keywords

references

Views

1744

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A new back-analysis method based on ν-SVR and improved PSO algorithm and its application

Regression analysis of 3D initial geostress in region of underground powerhouse for large hydropower station

Feedback identifying seepage parameters of 3D aquifer based on particle swarm optimization and support vector machine

Back analysis of initial seepage field of complex dam foundation at Shuangjiangkou Hydropower Station

Recognition of critical slip surface based on GA and SVM

Related Author

SUN Gang

TANG Zhong-min

ZHOU Zhong

XING Wan-bo

XIAO Ming-li

HE Jiang-da

XIE Hong-qiang

LIANG Bing

Related Institution

Chengdu Hydroelectric Investigation and Design Institute, China Hydropower Engineering Consulting Group Co.

Sichuan Institute of Water Resources and Hydroelectric Exploration Design and Research

School of Water Resources and Hydropower, Sichuan University

State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University

State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

AI问答

⁰