Domain-Informed Explainable AI for Suction Prediction in Xanthan Gum-Treated Clays

Abstract

Explainable artificial intelligence (XAI) is increasingly important in scientific and engineering applications where predictive performance alone is insufficient and model outputs must also be physically credible, transparent, and reliable under unseen conditions. This study proposes a domain-informed XAI framework for predicting total suction in xanthan gum-treated clays using 139 experimental observations covering different mineralogical, moisture, polymer-dosage, and curing conditions. Eleven linear, kernel-based, ensemble, boosting, and physics-guided algorithms were evaluated using leakage-resistant five-fold grouped cross-validation, including a matched constrained–unconstrained HGB comparison with identical model settings. The methodological contribution is an evidence-linked XAI validation protocol in which model explanations are not accepted from feature attribution alone, but are audited through their agreement with leakage-resistant grouped generalization, physically constrained response directions, matched experimental contrasts, residual behavior, predictive uncertainty, and applicability-domain support. Selective monotonic constraints, physics-guided residual learning, SHAP explanations, and nonlinear response visualization are integrated within this protocol as complementary sources of evidence rather than treated as independent indicators of interpretability. The unconstrained histogram–gradient-boosting model achieved the highest out-of-fold predictive performance (R2 = 0.958, RMSE = 0.098, and MAE = 0.070 in log10(MPa)). The corresponding monotonic model produced R2 = 0.935, RMSE = 0.121, and MAE = 0.094 but eliminated the directional violations detected in the unconstrained response, revealing a measurable trade-off between predictive accuracy and guaranteed physical consistency. Explanations identified moisture content as the dominant negative control and revealed that xanthan-gum effects were non-monotonic and dependent on curing, moisture, and mineralogy. The residual model remained interpretable but underperformed the leading ensembles. Overall, the framework validates explanations against experimental contrasts, physical directions, grouped generalization, residual behavior, uncertainty, and domain support, offering a transferable strategy for trustworthy XAI in structured scientific datasets.