M. Chwała, K. Phoon, M. Uzielli, Jie Zhang, Limin Zhang, J. Ching
{"title":"Time capsule for geotechnical risk and reliability","authors":"M. Chwała, K. Phoon, M. Uzielli, Jie Zhang, Limin Zhang, J. Ching","doi":"10.1080/17499518.2022.2136717","DOIUrl":null,"url":null,"abstract":"ABSTRACT This paper is motivated by the Time Capsule Project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and reliability are reviewed for the past six decades. The key features distinguishing geotechnical and structural engineering are the natural origin of the ground and the lack of sufficient data to characterize the ground using the more familiar frequentist interpretation of probability. For the first feature, random field theory is applied to model spatial variability and the random finite element method or other methods are proposed for solving soil-structure interaction problems in spatially variable soil. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and more recently for Bayesian machine learning. There is a gradual evolution towards reliability-based design because probabilistic methods offer a pathway to address big data and implement data-centric geotechnics as one step towards digital transformation. Given the complexity of the natural ground (known unknowns can be large and there are unknown unknowns), engineering judgment remains important to bridge the gap between theory and practice. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":"17 1","pages":"439 - 466"},"PeriodicalIF":6.5000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/17499518.2022.2136717","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}
引用次数: 11
Abstract
ABSTRACT This paper is motivated by the Time Capsule Project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and reliability are reviewed for the past six decades. The key features distinguishing geotechnical and structural engineering are the natural origin of the ground and the lack of sufficient data to characterize the ground using the more familiar frequentist interpretation of probability. For the first feature, random field theory is applied to model spatial variability and the random finite element method or other methods are proposed for solving soil-structure interaction problems in spatially variable soil. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and more recently for Bayesian machine learning. There is a gradual evolution towards reliability-based design because probabilistic methods offer a pathway to address big data and implement data-centric geotechnics as one step towards digital transformation. Given the complexity of the natural ground (known unknowns can be large and there are unknown unknowns), engineering judgment remains important to bridge the gap between theory and practice. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.
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.