An explainable intelligent system for multi‐performance shield tunnel tail grout optimization

Shield tunnel tail grouting fills the annular gap between excavated soil and tunnel lining, supporting soil stability and controlling ground settlements during construction. However, existing grout performance prediction methods are limited by labor-intensive empirical testing, insufficient datasets, and inadequate modeling of liquid-to-solid phase transitions, resulting in consolidation deformation, ground loss, suboptimal formulations, and increased settlements. To address these issues, this study develops an explainable intelligent system for multi-performance grout optimization, integrating innovative experimental testing with advanced machine learning. A unique database is constructed from liquid-state (e.g., density, bleeding rate, fluidity) and solid-state (e.g., unconfined compressive strength (UCS), compressed deformation) properties, augmented via a physics-constrained generative adversarial network for realistic datasets. Four algorithms (artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR)) are ensemble-optimized using Bayesian techniques and K-fold cross-validation, achieving high predictive accuracy. SHapley Additive exPlanations (SHAP) analysis enhances interpretability, identifying water–binder ratio as dominant for liquid properties and cement–fly ash ratio for strength. Laboratory experiments and field applications validate the system, highlighting its efficiency in grout optimization, accurate prediction of consolidation-induced settlements, and improved deformation control, thereby enabling better settlement management, protection of adjacent structures, and informed decision-making in shield tunneling projects.