Machine Learning for Data-Centric Geotechnics

Kok-Kwang Phoon, Chong Tang, Zi-Jun Cao

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781032886541
• Edition: 1
• eBook ISBN:
  9781040669501
• Topic: Technology & Engineering
• Subtopic: Civil Engineering
• Index: Technology & Engineering

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.

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• List of Contributors
• Chapter 1 Machine Learning in Offshore Geotechnical Engineering
• Chapter 2 Generative AI in Geotechnical Engineering: Current Landscape and Future Potential
• 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 Non-linear Structures in Geotechnical Data
• Chapter 6 Image-Based Paradigm for Geological Modeling
• 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 Feature 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 Shafts
• 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 Tunneling 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 Toward Real-Time Back Analysis in Tunnel Engineering
• Index