{"title":"Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering","authors":"Yu Wang, Hua-Ming Tian","doi":"10.1080/17499518.2023.2278136","DOIUrl":null,"url":null,"abstract":"ABSTRACTGeotechnical engineering is experiencing a paradigm shift towards digital transformation and intelligence, driven by Industry 4.0 and emerging digital technologies, such as machine learning. However, development and application of machine learning are relatively slow in geotechnical practice, because extensive training databases are a key to the success of machine learning, but geotechnical data are often small and ugly, leading to the difficulty in developing a suitable training database required for machine learning. In addition, convincing examples from real projects are rare that demonstrate the immediate added value of machine learning to geotechnical practices. To facilitate digital transformation and machine learning in geotechnical engineering, this study proposes to develop a project-specific training database that leverages on digital transformation of geotechnical workflow and reflects both project-specific data collected from various stages of the geotechnical workflow and domain knowledge in geotechnical practices, such as soil mechanics, numerical analysis principles, and prior engineering experience and judgment. A real ground improvement project is presented to illustrate the proposed method and demonstrate the added value of digital transformation and machine learning in geotechnical practices.KEYWORDS: Machine learningdata-centric geotechnicsdigital transformationdigital intelligencereal project example AcknowledgementsThe work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. CityU 11203322), a grant from the Innovation and Technology Commission of Hong Kong Special Administrative region (Project no: MHP/099/21), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":"43 13","pages":"0"},"PeriodicalIF":6.5000,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17499518.2023.2278136","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: 1
Abstract
ABSTRACTGeotechnical engineering is experiencing a paradigm shift towards digital transformation and intelligence, driven by Industry 4.0 and emerging digital technologies, such as machine learning. However, development and application of machine learning are relatively slow in geotechnical practice, because extensive training databases are a key to the success of machine learning, but geotechnical data are often small and ugly, leading to the difficulty in developing a suitable training database required for machine learning. In addition, convincing examples from real projects are rare that demonstrate the immediate added value of machine learning to geotechnical practices. To facilitate digital transformation and machine learning in geotechnical engineering, this study proposes to develop a project-specific training database that leverages on digital transformation of geotechnical workflow and reflects both project-specific data collected from various stages of the geotechnical workflow and domain knowledge in geotechnical practices, such as soil mechanics, numerical analysis principles, and prior engineering experience and judgment. A real ground improvement project is presented to illustrate the proposed method and demonstrate the added value of digital transformation and machine learning in geotechnical practices.KEYWORDS: Machine learningdata-centric geotechnicsdigital transformationdigital intelligencereal project example AcknowledgementsThe work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. CityU 11203322), a grant from the Innovation and Technology Commission of Hong Kong Special Administrative region (Project no: MHP/099/21), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).
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.