J. Zhang, Hongli Yao, Zi-peng Wang, Yaning Xue, Lu-lu Zhang
{"title":"用逆速度法预测边坡破坏时间","authors":"J. Zhang, Hongli Yao, Zi-peng Wang, Yaning Xue, Lu-lu Zhang","doi":"10.1080/17499518.2022.2132263","DOIUrl":null,"url":null,"abstract":"ABSTRACT The inverse velocity (INV) method is widely used for predicting the slope failure time. When applying the INV method, the inverse velocity can be assumed to be a linear and non-linear function of time, respectively, which are called linear and non-linear INV methods in this paper, respectively. Very few guidance is available in the literatures on the use of the two types of INV methods. In this paper, the performances of the linear and non-linear INV methods are assessed using a landslide database with 55 case histories. It is found that, two types of pitfalls may be encountered when applying the non-linear INV method, i.e. the saddle point and the ill-conditioned Hessian matrix. For the landslides examined in this paper, the linear INV method is free from the two pitfalls. When these pitfalls are encountered, the failure time predicted based on the non-linear INV methods may be significantly different from the actual slope failure time. For the landslides examined in this paper, the linear INV method is not only more stable, but also more accurate than the non-linear INV method. It is suggested that the linear INV method should be preferred over the non-linear INV method in future applications.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":"17 1","pages":"114 - 126"},"PeriodicalIF":6.5000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"On prediction of slope failure time with the inverse velocity method\",\"authors\":\"J. Zhang, Hongli Yao, Zi-peng Wang, Yaning Xue, Lu-lu Zhang\",\"doi\":\"10.1080/17499518.2022.2132263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT The inverse velocity (INV) method is widely used for predicting the slope failure time. When applying the INV method, the inverse velocity can be assumed to be a linear and non-linear function of time, respectively, which are called linear and non-linear INV methods in this paper, respectively. Very few guidance is available in the literatures on the use of the two types of INV methods. In this paper, the performances of the linear and non-linear INV methods are assessed using a landslide database with 55 case histories. It is found that, two types of pitfalls may be encountered when applying the non-linear INV method, i.e. the saddle point and the ill-conditioned Hessian matrix. For the landslides examined in this paper, the linear INV method is free from the two pitfalls. When these pitfalls are encountered, the failure time predicted based on the non-linear INV methods may be significantly different from the actual slope failure time. For the landslides examined in this paper, the linear INV method is not only more stable, but also more accurate than the non-linear INV method. It is suggested that the linear INV method should be preferred over the non-linear INV method in future applications.\",\"PeriodicalId\":48524,\"journal\":{\"name\":\"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards\",\"volume\":\"17 1\",\"pages\":\"114 - 126\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2022-11-17\",\"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/10.1080/17499518.2022.2132263\",\"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/10.1080/17499518.2022.2132263","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}
On prediction of slope failure time with the inverse velocity method
ABSTRACT The inverse velocity (INV) method is widely used for predicting the slope failure time. When applying the INV method, the inverse velocity can be assumed to be a linear and non-linear function of time, respectively, which are called linear and non-linear INV methods in this paper, respectively. Very few guidance is available in the literatures on the use of the two types of INV methods. In this paper, the performances of the linear and non-linear INV methods are assessed using a landslide database with 55 case histories. It is found that, two types of pitfalls may be encountered when applying the non-linear INV method, i.e. the saddle point and the ill-conditioned Hessian matrix. For the landslides examined in this paper, the linear INV method is free from the two pitfalls. When these pitfalls are encountered, the failure time predicted based on the non-linear INV methods may be significantly different from the actual slope failure time. For the landslides examined in this paper, the linear INV method is not only more stable, but also more accurate than the non-linear INV method. It is suggested that the linear INV method should be preferred over the non-linear INV method in future applications.
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.