Machine Learning for Data-Centric Geotechnics

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