{"title":"Report for ISSMGE TC309/TC304/TC222 Third ML dialogue on “Data-Driven Site Characterization (DDSC)”","authors":"K. Phoon, Z. Cao, Zhongqiang Liu, J. Ching","doi":"10.1080/17499518.2022.2105366","DOIUrl":null,"url":null,"abstract":"ABSTRACT The ISSMGE TC309/TC304/TC222 Third Machine Learning in Geotechnics Dialogue (3MLIGD) was hosted online by the Norwegian Geotechnical Institute on 3 December 2021. There is a consensus that the potential of digital transformation in geotechnical site characterisation is significant. Nonetheless, there is a clear-eyed recognition that the industry is currently governed by a set of rules that evolved from Industry 3.0 and it is only beginning to explore the potential of digital technologies. This state is to be expected as digital transformation is expected to change the “rules of the game” in the context of Industry 4.0 that is rapidly evolving in tandem with emerging technologies. The number of practitioners and researchers who are interested in data-centric geotechnics remains a small minority. There is a unanimous view that this small community can achieve greater impact and hasten progress by fostering more collaborations and working more closely together through: (1) data sharing, (2) creating a “yellow page” of people and projects to facilitate greater connectivity, (3) establishing novel collaborative modes between industry and academia, (4) demonstrating value through “ML supremacy” projects that include mapping studies covering large, real-time, multi-source datasets over large spatial domains, and (5) educating young talents by creating ML internships.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/17499518.2022.2105366","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: 7
Abstract
ABSTRACT The ISSMGE TC309/TC304/TC222 Third Machine Learning in Geotechnics Dialogue (3MLIGD) was hosted online by the Norwegian Geotechnical Institute on 3 December 2021. There is a consensus that the potential of digital transformation in geotechnical site characterisation is significant. Nonetheless, there is a clear-eyed recognition that the industry is currently governed by a set of rules that evolved from Industry 3.0 and it is only beginning to explore the potential of digital technologies. This state is to be expected as digital transformation is expected to change the “rules of the game” in the context of Industry 4.0 that is rapidly evolving in tandem with emerging technologies. The number of practitioners and researchers who are interested in data-centric geotechnics remains a small minority. There is a unanimous view that this small community can achieve greater impact and hasten progress by fostering more collaborations and working more closely together through: (1) data sharing, (2) creating a “yellow page” of people and projects to facilitate greater connectivity, (3) establishing novel collaborative modes between industry and academia, (4) demonstrating value through “ML supremacy” projects that include mapping studies covering large, real-time, multi-source datasets over large spatial domains, and (5) educating young talents by creating ML internships.
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.
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