长江科学院院报 ›› 2018, Vol. 35 ›› Issue (8): 78-83.DOI: 10.11988/ckyyb.20170132

• 岩土工程 • 上一篇    下一篇

基于极限学习机的边坡可靠度分析

宋永东1,2,苏立君1,2,3,张崇磊1,孙长宁1,2,屈新1,2   

  1. 1.中国科学院水利部成都山地灾害与环境研究所 山地灾害与地表过程重点实验室,成都 610041;
    2.中国科学院大学,北京 100049;
    3.中国科学院 青藏高原地球科学卓越创新中心,北京 100101
  • 收稿日期:2017-02-13 出版日期:2018-08-01 发布日期:2018-08-14
  • 通讯作者: 苏立君(1976-),男,辽宁清原人,研究员,博士,博士生导师,主要从事滑坡形成机理和破坏特征研究。E-mail:sulijun1976@163.com
  • 作者简介:宋永东(1990-),男,山东枣庄人,硕士研究生,主要从事边坡稳定可靠度方面的研究。E-mail:sydong345941794@163.com
  • 基金资助:
    中国科学院西部之光“一带一路”国际合作团队项目;国家自然科学基金项目(41761144077,51278397)

Reliability Analysis of Slopes Based on Extreme Learning Machine

SONG Yong-dong1,2, SU Li-jun1,2,3, ZHANG Chong-lei1,SUN Chang-ning1,2, QU Xin1,2   

  1. 1.Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, China;
    2.University of Chinese Academy of Sciences,Beijing 100049, China;
    3.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
  • Received:2017-02-13 Online:2018-08-01 Published:2018-08-14

摘要: 对于边坡极限状态函数无法显式表达的情况,传统可靠度分析方法存在求解困难或计算量大的弊端。提出了一种基于FLAC3D和极限学习机的边坡可靠度分析方法。利用均匀试验设计构造随机变量样本,基于FLAC3D强度折减法计算随机变量样本对应的安全系数;通过极限学习机强大的数据拟合能力映射出安全系数与随机变量之间的关系,构造响应面功能函数;将蒙特卡罗模拟生成的大量随机数代入响应面获得安全系数,在此基础上,计算边坡的失效概率与可靠度指标。通过具体算例分析,并与其他方法对比,发现本文方法结果可靠、易于实现,为边坡可靠度分析提供了一种新途径,具有广泛的应用前景。

关键词: 边坡可靠度, 极限学习机(ELM), 响应面功能函数, 强度折减法, 蒙特卡罗模拟, 失效概率

Abstract: As the limit state function of slope can't be explicitly expressed,conventional methods for slope reliability analysis are disadvantageous for difficulties and cumbersome calculation. A method for slope reliability analysis is proposed by combing the finite difference method of FLAC3D and the extreme learning machine (ELM). Samples of random variables are generated through uniform experimental design, and the safety factors of these random variables are calculated through the strength reduction method of FLAC3D. The mapping relationship between safety factors and random variables are obtained to construct the response surface function through the powerful fitting ability of ELM. Furthermore, a large number of random numbers generated by Monte-Carlo method are introduced into the function fitted by ELM to calculate the failure probability and reliability index of slope. Comparison with other methods through case study manifests that the proposed method is easy to be realized with reliable result.The research result provides a new approach for reliability analysis of slope, which is of broad application prospect.

Key words: slope reliability, extreme learning machine (ELM), response surface function, strength reduction method, Monte-Carlo simulation, failure probability

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