Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering

IF 6.5 3区工程技术 Q1 ENGINEERING, GEOLOGICAL

Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards Pub Date : 2023-11-06 DOI:10.1080/17499518.2023.2278136

Yu Wang, Hua-Ming Tian

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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).

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数字岩土工程:从数据驱动的场地特征到岩土工程的数字化转型和智能化

摘要在工业4.0和机器学习等新兴数字技术的推动下，岩土工程正经历着向数字化转型和智能化的范式转变。然而，机器学习在岩土工程实践中的发展和应用相对缓慢，因为广泛的训练数据库是机器学习成功的关键，但岩土工程数据往往又小又丑，导致难以开发出适合机器学习所需的训练数据库。此外，来自实际项目的令人信服的例子很少能证明机器学习对岩土工程实践的直接附加价值。为了促进岩土工程的数字化转型和机器学习，本研究建议开发一个项目特定的培训数据库，该数据库利用岩土工作流程的数字化转型，既反映了从岩土工作流程的各个阶段收集的项目特定数据，也反映了岩土工程实践中的领域知识，如土力学、数值分析原理和先前的工程经验和判断。通过一个实际的地面改善项目来说明所提出的方法，并展示了数字转换和机器学习在岩土工程实践中的附加价值。关键词:机器学习以数据为中心的岩土工程数字化转型数字智能项目示例致谢本文所描述的工作由香港特别行政区研究资助局资助(项目编号:城市大学11203322)，香港特别行政区创新科技委员会(项目编号:MHP/099/21)和深圳市科技创新委员会(深港澳科技项目(C类)编号:SGDX20210823104002020)资助。对财政支持表示感谢。披露声明作者未报告潜在的利益冲突。

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来源期刊

Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards Multiple-

CiteScore

8.70

自引率

10.40%

发文量

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.