Recognition of critical slip surface based on GA and SVM

ZHAO Hong-bo

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Recognition of critical slip surface based on GA and SVM

更新时间：2026-06-25

- Recognition of critical slip surface based on GA and SVM
- Vol. 27, Issue 11, (2006)
- 作者机构：
  
  1. 中国科学院岩土力学重点实验室,武汉,430071
  2. 绍兴文理学院土木系,绍兴,312000
- 作者简介：
- 基金信息：
- DOI：
  CLC： TB115
- Online First：04 December 2013，
  
  Published：2006
- 稿件说明：
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ZHAO Hong-bo. Recognition of critical slip surface based on GA and SVM[J]. 2006, 27(11).
DOI：

ZHAO Hong-bo. Recognition of critical slip surface based on GA and SVM[J]. 2006, 27(11). DOI：

摘要

结合支持向量机与遗传算法，提出了一种边坡最危险滑动面识别的新方法。这种方法基于正交设计和极限平衡分析获得学习样本，通过支持向量机学习，从而获得最危险滑动面与安全系数之间的非线性映射关系，再用遗传算法从全局空间上搜索，进行最危险滑动面的识别，并同时获得对应的最小安全系数，该方法计算速度快、效率高。给出了两个算例，结果是令人满意的。

Abstract

A new approach to recognize critical slip surface is proposed by combining the support vector machine and genetic algorithm.The learning and testing samples produced in orthogonal experiment are used to train the support vector machine. Thus

the support vector machine is used to describe the relationship between slip surface and factor of safety. Then genetic algorithm is adopted to search critical slip surface in their global ranges. This approach was applied to two examples. The results are satisfactory.

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Related Institution

Department of Mechanics and Engineering Sciences, Liaoning Technical University

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