期刊文献+

基于克里金模型的边坡稳定可靠度分析方法 被引量:16

An analytic method for slope stability reliability based on Kriging model
下载PDF
导出
摘要 边坡工程的复杂性不仅表现为各岩土参数的变异性和非确定性,而且还在于其极限状态功能函数的非解析性及隐式性。以Janbu法为例,研究了隐式功能函数下易于执行的边坡工程稳定可靠度计算方法。首先,调用边坡极限平衡模式获得岩土基本参数,并利用拉丁超立方试验设计抽取影响边坡稳定性基本参数的适量初始样本。其次,采用地质统计学中的克里金(Kriging)各向异性关联映射方法,将边坡功能函数值表述为随机过程。然后,结合主动学习方法,基于搜索规则调整训练样本,通过反复迭代循环确定满足实际工程精度的随机过程所表示的边坡功能函数。最后,调用随机过程函数通过验算点法(JC法)获得边坡的失效概率。工程算例分析表明,文中方法的求解精度与蒙特卡洛模拟方法相当,但计算过程简明,效率高,更具工程实用性。 The complexity of slope engineering is not only reflected in the variability of geotechnical parameters, but also in the implicit and nonanalytic properties of the performance function. Therefore, the direct calculation of slope stability reliability under implicit performance function that based on Janbu method is researched. First, the slope limit equilibrium model is called to obtain basic geotechnical parameters, and then the initial samples that affect slope stability is acquired through the Latin hypercube sampling. Secondly, using the Kriging anisotropic association mapping method dependence mapping method, the slope function value is expressed as a function of the random process. Then combined with the active learning method, and based on the searching rules to adjust the training samples, the slope performance functions denoted by the random process that meet the accuracy of the actual project is determined through iterative cycle. Finally, the random process function is called to work out the failure probability of slopes through the checking point method(JC method). The case study shows that the accuracy of the proposed method is equivalent to that of the Monte Carlo simulation; but the calculation process is simple, highly efficient, and more practicable.
出处 《岩土力学》 EI CAS CSCD 北大核心 2015年第S1期439-444,共6页 Rock and Soil Mechanics
基金 国家自然科学基金(No.51108397) 湖南省自然科学基金(No.2015JJ2136) 湘潭大学人才引进基金(No.KZ08026)
关键词 边坡工程 克里金(Krigin)模型 拉丁超立方设计 最可能失效区域 主动学习 slope engineering Kriging model Latin hypercube design most failure zone active learning
  • 相关文献

参考文献15

  • 1谢延敏.基于Kriging模型和灰色关联分析的板料成形工艺稳健优化设计研究[D].上海交通大学2007 被引量:1
  • 2B. Echard,N. Gayton,M. Lemaire.AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation[J]. Structural Safety . 2011 (2) 被引量:1
  • 3Junho Won,Changhyun Choi,Jooho Choi.Improved dimension reduction method (DRM) in uncertainty analysis using kriging interpolation[J]. Journal of Mechanical Science and Technology . 2009 (5) 被引量:1
  • 4Irfan Kaymaz.Application of kriging method to structural reliability problems[J]. Structural Safety . 2004 (2) 被引量:2
  • 5Vladimir Cherkassky,Yunqian Ma.Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks . 2003 (1) 被引量:1
  • 6Herbert Martins Gomes,Armando Miguel Awruch.Comparison of response surface and neural network with other methods for structural reliability analysis[J]. Structural Safety . 2003 (1) 被引量:1
  • 7Donald R. Jones,Matthias Schonlau,William J. Welch.Efficient Global Optimization of Expensive Black-Box Functions[J]. Journal of Global Optimization . 1998 (4) 被引量:1
  • 8G Riccardi,D Hakkani-Tur.Active Learning: Theory and Applications to Automatic Speech Recognition. IEEE Transactions on Speech and Audio Processing . 2005 被引量:1
  • 9Janbu N.Slope stability computations. Embankment Dam Engineering, Casagrande Memorial Volume . 1973 被引量:1
  • 10Soren N Lophaven,Hans Bruun Nielsen,Jacob Sondergaard.Aspects of the Matlab toolbox DACE. Technical Report IMM-REP-2002-13, Informatics and mathematical modeling . 2002 被引量:1

共引文献4

同被引文献114

引证文献16

二级引证文献61

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部