Description

This theme explores the application of artificial intelligence, machine learning, deep learning, and advanced computational intelligence to geotechnical material characterization, engineering prediction, numerical modelling, uncertainty assessment, and infrastructure decision-making. It encompasses artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machines (SVM), random forests (RF), gradient boosting algorithms, multivariate adaptive regression splines (MARS), ensemble learning, deep neural networks, physics-informed neural networks (PINNs), and emerging foundation models. Particular emphasis is placed on integrating geological knowledge, soil and rock mechanics, experimental measurements, field investigation data, and governing physical principles into reliable computational prediction frameworks.