Improved support point selection on adaptive kriging metamodels for reliability analysis of soil slopes

IF 3.3 2区工程技术 Q2 ENGINEERING, GEOLOGICAL Soils and Foundations Pub Date : 2023-10-21 DOI:10.1016/j.sandf.2023.101380

C. Arévalo , R.O. Ruiz , Y. Alberto

{"title":"Improved support point selection on adaptive kriging metamodels for reliability analysis of soil slopes","authors":"C. Arévalo , R.O. Ruiz , Y. Alberto","doi":"10.1016/j.sandf.2023.101380","DOIUrl":null,"url":null,"abstract":"<div><p>The paper presents an iterative approach based on stochastic simulations and adaptive Kriging metamodels to perform reliability and safety assessments of soil slopes. Two new rules for adaptively selecting support points are proposed, considering an entropy learning function and the closeness to the failure domain defined by a limit state function. In addition, a stopping criterion is proposed based on root-mean-square and mean absolute percentage errors computed with cross-validation at the local level, focusing on regions where the uncertainties are relevant. Finally, the selection rules for support points and the error metrics are implemented in two benchmark problems with a low, moderate, and high probability of failure. Ultimately, the work leads to an adaptive Kriging strategy for slope stability assessment, offering: (1) a fair comparison with other strategies based on a significant number of realizations, (2) a stopping criteria based on a new local error metric, (3) an insight of the behavior across different magnitudes of the probability of failure, and (4) a new selection rule that reduces the total number of support points significantly. The proposed scheme is easily paired with commercial software to compute support points, resulting in an attractive tool for practitioners.</p></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"63 6","pages":"Article 101380"},"PeriodicalIF":3.3000,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0038080623001099/pdfft?md5=f507cedb6a5603ab3018124094b48421&pid=1-s2.0-S0038080623001099-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soils and Foundations","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038080623001099","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}

引用次数: 0

Abstract

The paper presents an iterative approach based on stochastic simulations and adaptive Kriging metamodels to perform reliability and safety assessments of soil slopes. Two new rules for adaptively selecting support points are proposed, considering an entropy learning function and the closeness to the failure domain defined by a limit state function. In addition, a stopping criterion is proposed based on root-mean-square and mean absolute percentage errors computed with cross-validation at the local level, focusing on regions where the uncertainties are relevant. Finally, the selection rules for support points and the error metrics are implemented in two benchmark problems with a low, moderate, and high probability of failure. Ultimately, the work leads to an adaptive Kriging strategy for slope stability assessment, offering: (1) a fair comparison with other strategies based on a significant number of realizations, (2) a stopping criteria based on a new local error metric, (3) an insight of the behavior across different magnitudes of the probability of failure, and (4) a new selection rule that reduces the total number of support points significantly. The proposed scheme is easily paired with commercial software to compute support points, resulting in an attractive tool for practitioners.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于自适应kriging元模型的边坡可靠度分析支撑点选择改进

本文提出了一种基于随机模拟和自适应Kriging元模型的迭代方法来进行土质边坡的可靠性和安全性评估。考虑了熵学习函数和极限状态函数定义的故障域的接近度，提出了两种自适应选择支撑点的规则。此外，提出了一种基于局部交叉验证计算的均方根和平均绝对百分比误差的停止准则，重点关注不确定性相关的区域。最后，在低、中、高故障概率的两个基准问题中实现了支撑点的选择规则和误差度量。最终，这项工作产生了一种用于边坡稳定性评估的自适应Kriging策略，提供:(1)基于大量实现的与其他策略的公平比较，(2)基于新的局部误差度量的停止标准，(3)对不同程度的破坏概率的行为的洞察，以及(4)一个新的选择规则，显着减少支点总数。该方案很容易与商业软件配对来计算支撑点，对从业者来说是一个有吸引力的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Soils and Foundations 工程技术-地球科学综合

CiteScore

6.40

自引率

8.10%

发文量

审稿时长

5 months

期刊介绍： Soils and Foundations is one of the leading journals in the field of soil mechanics and geotechnical engineering. It is the official journal of the Japanese Geotechnical Society (JGS)., The journal publishes a variety of original research paper, technical reports, technical notes, as well as the state-of-the-art reports upon invitation by the Editor, in the fields of soil and rock mechanics, geotechnical engineering, and environmental geotechnics. Since the publication of Volume 1, No.1 issue in June 1960, Soils and Foundations will celebrate the 60th anniversary in the year of 2020. Soils and Foundations welcomes theoretical as well as practical work associated with the aforementioned field(s). Case studies that describe the original and interdisciplinary work applicable to geotechnical engineering are particularly encouraged. Discussions to each of the published articles are also welcomed in order to provide an avenue in which opinions of peers may be fed back or exchanged. In providing latest expertise on a specific topic, one issue out of six per year on average was allocated to include selected papers from the International Symposia which were held in Japan as well as overseas.