Interpretable AI for Site-Adaptive Soil Liquefaction Assessment

Soil liquefaction remains a critical geotechnical hazard during earthquakes, posing significant risks to infrastructure and urban resilience. Traditional empirical methods, while practical, often fall short in capturing complex parameter interactions and providing interpretable outputs. This study presents an interpretable machine learning (IML) framework for soil liquefaction assessment using Rough Set Theory (RST) to generate a transparent, rule-based predictive model. Leveraging a standardized SPT-based case history database, the model induces IF–THEN rules that relate seismic and geotechnical parameters to liquefaction occurrence. The resulting 25-rule set demonstrated an accuracy of 86.2% and strong alignment (93.8%) with the widely used stress-based semi-empirical model. Beyond predictive performance, the model introduces scenario maps and parameter interaction diagrams that elucidate key thresholds and interdependencies, enhancing its utility for engineers, planners, and policymakers. Notably, the model reveals that soils with high fines content can still be susceptible to liquefaction under strong shaking, and that epicentral distance plays a more direct role than previously emphasized. By balancing interpretability and predictive strength, this rule-based approach advances site-adaptive, explainable, and technically grounded liquefaction assessment—bridging the gap between traditional methods and intelligent decision support in geotechnical engineering.