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边坡临界滑面搜索的改进粒子群优化算法

杨善统, 姜清辉, 尹涛, 姚池, 陈娜, 周彬

杨善统, 姜清辉, 尹涛, 姚池, 陈娜, 周彬. 边坡临界滑面搜索的改进粒子群优化算法[J]. 岩土工程学报, 2015, 37(8): 1411-1417. DOI: 10.11779/CJGE201508008
引用本文: 杨善统, 姜清辉, 尹涛, 姚池, 陈娜, 周彬. 边坡临界滑面搜索的改进粒子群优化算法[J]. 岩土工程学报, 2015, 37(8): 1411-1417. DOI: 10.11779/CJGE201508008
shu
YANG Shan-tong, JIANG Qing-hui, YIN Tao, YAO Chi, CHEN Na, ZHOU Bin. Search of critical slip surface of slopes using improved particle swarm optimization method[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(8): 1411-1417. DOI: 10.11779/CJGE201508008
Citation: YANG Shan-tong, JIANG Qing-hui, YIN Tao, YAO Chi, CHEN Na, ZHOU Bin. Search of critical slip surface of slopes using improved particle swarm optimization method[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(8): 1411-1417. DOI: 10.11779/CJGE201508008
shu

边坡临界滑面搜索的改进粒子群优化算法  English Version

  • 1. 武汉大学土木建筑工程学院,湖北 武汉 430072;
    2.南昌大学建筑工程学院,江西 南昌 330031;
    3. 中铁二院重庆勘察设计研究院,重庆 401121

基金项目: 国家重点基础研究发展计划（“973”项目）（2011CB013506）; 重庆市应用开发计划重点项目（cstc2014yykfB30003）
详细信息
    作者简介:

    杨善统(1989- ),男,硕士研究生,主要从事边坡稳定分析。E-mail: 952811382@qq.com。

  • 中图分类号: P642

Search of critical slip surface of slopes using improved particle swarm optimization method

  • 1. School of Civil and Architectural Engineering, Wuhan University, Wuhan 430072, China;
    2. School of Architecture and Civil Engineering, Nanchang University, Nanchang 330031, China;
    3. China Railway Eryuan Engineering Group Co., Ltd., Chongqing 401121, China

摘要

    摘要
  • 摘要: 边坡临界滑面的确定对边坡稳定分析和加固设计极为重要,采用基于变异和二次序列规划的改进粒子群优化算法（VSPSO）进行临界滑面搜索。VSPSO算法中通过变异操作增强粒子群跳出局部最优解的能力,并用二次序列规划（SQP）加速局部搜索,大大提高了粒子群获得全局最优的能力。通过对有解析解的边坡算例进行分析,验证了该算法的准确性及优越性;对澳大利亚计算机应用协会（ACADS）提供的均质边坡、多层土边坡以及含软弱层边坡进行分析,结果表明改进的VSPSO算法搜索所得滑面比传统PSO算法更逼近推荐答案,具有更好的鲁棒性,而且随着边坡复杂程度的增加,更能体现改进VSPSO算法的优越性,具有广阔的应用前景。
    Abstract: The location of the critical slip surface is a very important issue in slope stability analysis and reinforcement design. In this study, an improved particle swarm optimization (VSPSO) algorithm is proposed to search for the critical slip surface based on particle variation (PV) and sequential quadratic programming (SQP). PV enhances the ability of PSO in jumping out of the local optimum, and SQP accelerates local search. The combination of PV and SQP greatly promotes the capacity of the original PSO in looking for the global optimum. An example with analytical solution is analyzed by the VSPSO, and the results demonstrate the accuracy and efficiency of the proposed model. Three typical examples from ACADS are then given, which are respectively homogeneous slope, multilayer soil slope and slope with weak layer. It is shown that results from the VSPSO are more approximate to the recommended values than those from the PSO. Furthermore, the VSPSO has a quite well robustness for the slopes with very complicated geometries and material properties.
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    • 参考文献(28)
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  • 收稿日期:  2014-10-06
  • 发布日期:  2015-08-24

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