Explainable AI for predicting the strength of bio-cemented sands

The biological stabilization of soil using microbially induced carbonate precipitation (MICP) employs ureolytic bacteria to precipitate calcium carbonate (CaCO 3 ), which binds soil particles, enhancing strength, stiffness, and erosion resistance. The unconfined compressive strength (UCS), a key measure of soil strength, is critical in geotechnical engineering as it directly reflects the mechanical stability of treated soils. This study integrates explainable artificial intelligence (XAI) with geotechnical insights to model the UCS of MICP-treated sands. Using 517 experimental data points and a combination of various input variables—including median grain size ( D 50 ), coefficient of uniformity ( C u ), void ratio ( e ), urea concentration ( M u ), calcium concentration ( M c ), optical density ( OD ) of bacterial solution, pH, and total injection volume ( V t )— five machine learning (ML) models, including eXtreme gradient boosting (XGBoost), Light gradient boosting machine (LightGBM), random forest (RF), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were developed and optimized. The ensemble models (XGBoost, LightGBM, and RF) were optimized using the Chernobyl disaster optimizer (CDO), a recently developed metaheuristic algorithm. Of these, LightGBM-CDO achieved the highest accuracy for UCS prediction. XAI techniques like feature importance analysis (FIA), SHapley additive exPlanations (SHAP), and partial dependence plots (PDPs) were also used to investigate the complex non-linear relationships between the input and output variables. The results obtained have demonstrated that the XAI-driven models can enhance the predictive accuracy and interpretability of MICP processes, offering a sustainable pathway for optimizing geotechnical applications.