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。
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 FLAC3 Dand 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 FLAC3 D. 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.
作者
宋永东
苏立君
张崇磊
孙长宁
屈新
SONG Yong-dong;SU Li-jun;ZHANG Chong-lei;SUN Chang-ning;Qu Xin(Key Laboratocy of Mountain Hazards and Earth Surface Process,Institute of Mountain Hazards and Environment,CAS,Chengdu 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China;CAS Center for Excellence in Tibetan Plateau Earth Sciences,Beijing 100101,China)
出处
《长江科学院院报》
CSCD
北大核心
2018年第8期78-83,共6页
Journal of Changjiang River Scientific Research Institute
基金
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)
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
slope reliability
extreme learning machine （ELM）
response surface function
strength reduction method
Monte-Carlo simulation
failure probability
