{"title":"基于自适应kriging元模型的边坡可靠度分析支撑点选择改进","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":"{\"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}","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}
Improved support point selection on adaptive kriging metamodels for reliability analysis of soil slopes
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.
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.
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