Digital transformation in geotechnical site characterization: trends, methods, and future roadmaps

The reliability and sustainability of civil infrastructure depend on high-resolution subsurface characterization, yet conventional site investigation is often limited by labor-intensive fieldwork and sparse measurements. This critical systematic review links the digital transformation of geological surveying to geotechnical site characterization, treating remote sensing, geophysics, and digital geological mapping as upstream evidence that constrains engineering interpretation. We synthesize how multi-source observations are converted into engineering-ready site models and decision-facing uncertainty for design and risk management. Through analyzing the intellectual structure of the field, we identify a shift toward integrated workflows that combine multi-platform sensing with physics-informed and explainable AI. We discuss where these workflows are mature enough to support engineering decisions (e.g. ground model updating, hazard screening for slopes, and construction-phase monitoring), and where they remain limited by transferability, registration errors, and validation constraints. The analysis reveals that the frontier is shifting beyond data acquisition toward four-dimensional geological digital twins that can be updated through monitoring and consumed within BIM-centered delivery. This review offers a roadmap for integrating smart sensing and AI into routine engineering practice, highlighting the necessity of explainable algorithms to ensure safety and resilience in the built environment.