Hybrid AI-Physics Modeling in Geotechnical Engineering: A Next-Generation Review of Theory-Guided Machine Learning for Smart and Resilient Infrastructure

Machine learning (ML) has emerged as a transformative tool in geotechnical engineering, offering data-driven alternatives to traditional empirical and analytical approaches. This paper presents a critical narrative review of recent advances in theory-guided machine learning (TGML) applications in geotechnical engineering, including soil-property prediction, constitutive modelling, pile capacity prediction, seismic soil–structure interaction, foundation performance, and slope stability. The review organizes the literature according to the physical mechanisms through which theoretical knowledge is incorporated into machine-learning methods, including physics-guided features, hybrid constitutive models, physics-informed neural networks, physics-constrained surrogates, probabilistic hybrid frameworks, and multiscale models. Emerging trends such as hybrid physics-informed models, explainable artificial intelligence (AI) frameworks, and integration with numerical simulations, real-time monitoring and data assimilation, and digital-twin-enabled infrastructure assessment are also discussed. The paper identifies key research gaps and proposes future directions for improving model robustness, reliability, and practical implementation in engineering design and infrastructure management. This review provides a structured roadmap for advancing AI-driven methodologies in geotechnical engineering, and their integration into smart and resilient infrastructure systems, while highlighting the critical issues that must be addressed for broader adoption in practice.