Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis  

基于Jensen-Shannon散度的自适应采样方法的高效可靠性分析

作　　者：CHEN Liang-jun HONG Yu Sujith MANGALATHU GOU Hong-ye PU Qian-hui 陈良军;洪彧;Sujith MANGALATHU;勾红叶;蒲黔辉(Department of Bridge Engineering,School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;National Engineering Laboratory for Technology of Geological Disaster Prevention in Land Transportation,Southwest Jiaotong University,Chengdu 611756,China;Research Scientist,Equifax Inc,A lpharetta 30309,USA)

机构地区：[1]Department of Bridge Engineering,School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China [2]National Engineering Laboratory for Technology of Geological Disaster Prevention in Land Transportation,Southwest Jiaotong University,Chengdu 611756,China [3]Research Scientist,Equifax Inc,A lpharetta 30309,USA

出　　处：《Journal of Central South University》2021年第8期2407-2422,共16页中南大学学报（英文版）

基　　金：Project(KY201801005)supported by the China-Indonesia High-Speed Rail Technology Joint Research Center。

摘　　要：Extensive studies have been carried out for reliability studies on the basis of the surrogate model,which has the advantage of guaranteeing evaluation accuracy while minimizing the need of calling the real yet complicated performance function.Here,one novel adaptive sampling approach is developed for efficiently estimating the failure probability.First,one innovative active learning function integrating with Jensen-Shannon divergence(JSD)is derived to update the Kriging model by selecting the most suitable sampling point.For improving the efficient property,one trust-region method receives the development for reducing computational burden about the evaluation of active learning function without compromising the accuracy.Furthermore,a termination criterion based on uncertainty function is introduced to achieve better robustness in different situations of failure probability.The developed approach shows two main merits:the newly selected sampling points approach to the area of limit state boundary,and these sampling points have large discreteness.Finally,three case analyses receive the conduction for demonstrating the developed approach s feasibility and performance.Compared with Monte Carlo simulation or other active learning functions,the developed approach has advantages in terms of efficiency,convergence,and accurate when dealing with complex problems.目前,对基于代理模型的可靠度分析进行了广泛的研究,该方法在保证评估准确性的同时,减少调用真实且复杂性能函数的次数。为了更高效地评估失效概率,本文提出了一种自适应采样方法。首先,结合Jensen-Shannon散度(JSD)推导了一种新的主动学习函数,通过选择最合适的采样点更新Kriging模型。为了提高效率,建立了一种信念域方法,用以减少有关主动学习功能函数评估时的计算负担,同时不影响评估的准确性。此外,引入了基于不确定性函数的终止准则,以确保在不同的失效概率情况下实现更好的鲁棒性。该方法主要有以下两个优点:新选择的采样点接近极限状态边界区域,且这些采样点具有较大的离散性。最后,通过三个案例分析验证了方法的可行性和性能。结果表明,与蒙特卡罗模拟或其他主动学习函数相比,该方法在处理复杂问题时在效率、收敛性和准确性方面均具有优势。

关 键 词：RELIABILITY Monte Carlo Kriging model Jensen-Shannon divergence TRUST-REGION 

分 类 号：TB114.3[理学—概率论与数理统计]