Hui Liu, Junjie Zheng, Rongjun Zhang, Wenyu Yang, Y. Guo
{"title":"空间变化土壤中边坡系统的代表性滑面识别与可靠性分析","authors":"Hui Liu, Junjie Zheng, Rongjun Zhang, Wenyu Yang, Y. Guo","doi":"10.1080/17499518.2022.2112697","DOIUrl":null,"url":null,"abstract":"ABSTRACT A slope system is a series system with numerous potential slip surfaces (PSSs), and its failure probability is commonly evaluated by several significant failure surfaces, or representative slip surfaces (RSSs). Previous efforts have mainly identified the RSSs in spatially variable soils from the perspective of the correlations between different PSSs, the effects of the failure probabilities of the PSSs were rarely considered. With the goal of identifying RSSs from the perspective of the system failure probability, a method adopting the second-order reliability method (SORM) and the multimodal optimisation is proposed. In this method, the spatial variability of soil properties along the slip surface is characterised by local averaging to reduce the number of variables in SORM. Equations for calculating the correlation coefficient between different PSSs with correlated variables are derived. The task of RSS identification is transformed as a multimodal optimisation problem, and the PSSs that make great contributions to the system failure probability are determined as RSSs. The proposed method and the derived equations are demonstrated using two slope examples. The results show that the proposed method is capable of identifying RSSs with significant contributions, and it provides a proper estimate of the system failure probability.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":"17 1","pages":"503 - 520"},"PeriodicalIF":6.5000,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Representative slip surface identification and reliability analysis of slope systems in spatially variable soils\",\"authors\":\"Hui Liu, Junjie Zheng, Rongjun Zhang, Wenyu Yang, Y. Guo\",\"doi\":\"10.1080/17499518.2022.2112697\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT A slope system is a series system with numerous potential slip surfaces (PSSs), and its failure probability is commonly evaluated by several significant failure surfaces, or representative slip surfaces (RSSs). Previous efforts have mainly identified the RSSs in spatially variable soils from the perspective of the correlations between different PSSs, the effects of the failure probabilities of the PSSs were rarely considered. With the goal of identifying RSSs from the perspective of the system failure probability, a method adopting the second-order reliability method (SORM) and the multimodal optimisation is proposed. In this method, the spatial variability of soil properties along the slip surface is characterised by local averaging to reduce the number of variables in SORM. Equations for calculating the correlation coefficient between different PSSs with correlated variables are derived. The task of RSS identification is transformed as a multimodal optimisation problem, and the PSSs that make great contributions to the system failure probability are determined as RSSs. The proposed method and the derived equations are demonstrated using two slope examples. The results show that the proposed method is capable of identifying RSSs with significant contributions, and it provides a proper estimate of the system failure probability.\",\"PeriodicalId\":48524,\"journal\":{\"name\":\"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards\",\"volume\":\"17 1\",\"pages\":\"503 - 520\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2022-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/17499518.2022.2112697\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/17499518.2022.2112697","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Representative slip surface identification and reliability analysis of slope systems in spatially variable soils
ABSTRACT A slope system is a series system with numerous potential slip surfaces (PSSs), and its failure probability is commonly evaluated by several significant failure surfaces, or representative slip surfaces (RSSs). Previous efforts have mainly identified the RSSs in spatially variable soils from the perspective of the correlations between different PSSs, the effects of the failure probabilities of the PSSs were rarely considered. With the goal of identifying RSSs from the perspective of the system failure probability, a method adopting the second-order reliability method (SORM) and the multimodal optimisation is proposed. In this method, the spatial variability of soil properties along the slip surface is characterised by local averaging to reduce the number of variables in SORM. Equations for calculating the correlation coefficient between different PSSs with correlated variables are derived. The task of RSS identification is transformed as a multimodal optimisation problem, and the PSSs that make great contributions to the system failure probability are determined as RSSs. The proposed method and the derived equations are demonstrated using two slope examples. The results show that the proposed method is capable of identifying RSSs with significant contributions, and it provides a proper estimate of the system failure probability.
期刊介绍:
Georisk covers many diversified but interlinked areas of active research and practice, such as geohazards (earthquakes, landslides, avalanches, rockfalls, tsunamis, etc.), safety of engineered systems (dams, buildings, offshore structures, lifelines, etc.), environmental risk, seismic risk, reliability-based design and code calibration, geostatistics, decision analyses, structural reliability, maintenance and life cycle performance, risk and vulnerability, hazard mapping, loss assessment (economic, social, environmental, etc.), GIS databases, remote sensing, and many other related disciplines. The underlying theme is that uncertainties associated with geomaterials (soils, rocks), geologic processes, and possible subsequent treatments, are usually large and complex and these uncertainties play an indispensable role in the risk assessment and management of engineered and natural systems. Significant theoretical and practical challenges remain on quantifying these uncertainties and developing defensible risk management methodologies that are acceptable to decision makers and stakeholders. Many opportunities to leverage on the rapid advancement in Bayesian analysis, machine learning, artificial intelligence, and other data-driven methods also exist, which can greatly enhance our decision-making abilities. The basic goal of this international peer-reviewed journal is to provide a multi-disciplinary scientific forum for cross fertilization of ideas between interested parties working on various aspects of georisk to advance the state-of-the-art and the state-of-the-practice.