Machine Learning for Data-Centric Geotechnics
1st Edition
Will be published approx. on 29. July 2026
456 pages
E-Book
978-1-040-52121-2 (ISBN)
Description
Machine learning and other digital technologies fed with large datasets offer a major set of tools for practical geotechnical design. Large language models and other generative AIs can perform cognitive tasks currently undertaken by humans -- and might even predict the next event based on some time series. This depends on a balance of data centricity, fit-for (and transform) practice, and geotechnical context, and can be achieved by the integration of information, data, techniques, tools, perspectives, concepts, theories, along with experience from both geotechnical engineering and machine learning in computer science. And yet good engineering and research outcomes are still dependent on how practice (which includes the workforce) is improved or even transformed in the longer term to better serve end-users. This collection of focused chapters from a group of specialists presents principles and broad up to date practice of machine learning, along with a number of example areas of site characterization, design and construction in geotechnics.
This book is essential for sophisticated practitioners as well as graduate students.
This book is essential for sophisticated practitioners as well as graduate students.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Illustrations
63 Tables, black and white; 190 Line drawings, color; 21 Line drawings, black and white; 36 Halftones, color; 226 Illustrations, color; 21 Illustrations, black and white
ISBN-13
978-1-040-52121-2 (9781040521212)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kok-Kwang Phoon | Chong Tang | Zi-Jun Cao
Machine Learning for Data-Centric Geotechnics
Book
approx. 07/2026
1st Edition
CRC Press
€216.50
Not yet published
Persons
Kok-Kwang Phoon is President designate of Singapore University of Technology and Design. He has edited or written several books with CRC Press, including Model Uncertainties in Foundation Design. He was awarded the ASCE Norman Medal twice in 2005 and 2020, and is the Founding Editor of Georisk.
Chong Tang is a Professor of Dalian University of Technology in China. He was awarded ASCE's Norman Medal in 2020.
Zi-Jun Cao is Professor at Southwest Jiaotong University, China. He received the GEOSNet Young Researcher Award in 2022 and ISSMGE Bright Spark Lecture Award in 2019.
Chong Tang is a Professor of Dalian University of Technology in China. He was awarded ASCE's Norman Medal in 2020.
Zi-Jun Cao is Professor at Southwest Jiaotong University, China. He received the GEOSNet Young Researcher Award in 2022 and ISSMGE Bright Spark Lecture Award in 2019.
Content
Chapter 1 Machine Learning in Offshore Geotechnical Engineering
Chapter 2 Generative AI in Geotechnical Engineering: Current
Chapter 3 Addressing the site recognition challenge using tailored clustering
Chapter 4 Machine Learning for the Classification of Natural Sands
Chapter 5 Deep Insight into the Minimum Information Dependence Model for Uncovering Nonlinear Structures in Geotechnical Data
Chapter 6 Image-based Paradigm for Geological Modelling
Chapter 7 Data-Driven Geological Modeling and Uncertainty Quantification Using Bayesian Machine Learning and Stochastic Simulation
Chapter 8 Bayesian Hierarchical Modeling for Geotechnical Data Analysis
Chapter 9 Development of the optimal Bayesian Gaussian process regression models for prediction of geotechnical properties with features selection
Chapter 10 Auto-ML for Model Calibration and Selection in Geotechnical Engineering: General Framework and Application to Constitutive Parameter Estimation for Materials Following the NorSand Model
Chapter 11 Optimizing Machine Learning for Regression Tasks: Estimating the Axial Capacity of Drilled Shaf
Chapter 12 Physics-informed Sparse Machine Learning of Geotechnical Monitoring Data
Chapter 13 Data-driven risk assessment and prediction of deep excavation
Chapter 14 Leveraging machine learning for optimizing TBM tunnelling operations: a big data approach using in-situ and field data
Chapter 15 Revolution or Risk? The Dual Edges of Machine Learning and Stochastic Modeling in Tunnel Construction
Chapter 16 Towards real-time back analysis in tunnel engineering
Chapter 2 Generative AI in Geotechnical Engineering: Current
Chapter 3 Addressing the site recognition challenge using tailored clustering
Chapter 4 Machine Learning for the Classification of Natural Sands
Chapter 5 Deep Insight into the Minimum Information Dependence Model for Uncovering Nonlinear Structures in Geotechnical Data
Chapter 6 Image-based Paradigm for Geological Modelling
Chapter 7 Data-Driven Geological Modeling and Uncertainty Quantification Using Bayesian Machine Learning and Stochastic Simulation
Chapter 8 Bayesian Hierarchical Modeling for Geotechnical Data Analysis
Chapter 9 Development of the optimal Bayesian Gaussian process regression models for prediction of geotechnical properties with features selection
Chapter 10 Auto-ML for Model Calibration and Selection in Geotechnical Engineering: General Framework and Application to Constitutive Parameter Estimation for Materials Following the NorSand Model
Chapter 11 Optimizing Machine Learning for Regression Tasks: Estimating the Axial Capacity of Drilled Shaf
Chapter 12 Physics-informed Sparse Machine Learning of Geotechnical Monitoring Data
Chapter 13 Data-driven risk assessment and prediction of deep excavation
Chapter 14 Leveraging machine learning for optimizing TBM tunnelling operations: a big data approach using in-situ and field data
Chapter 15 Revolution or Risk? The Dual Edges of Machine Learning and Stochastic Modeling in Tunnel Construction
Chapter 16 Towards real-time back analysis in tunnel engineering
