An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling

This study proposes an interpretable and variance-aware sensitivity analysis framework for analyzing the compressive strength of rice husk ash concrete under correlated input conditions, using statistically consistent virtual sampling and surrogate-based sensitivity analysis. A Gaussian copula is used to construct a virtual design space that preserves empirical marginal distributions and inter-variable dependencies. An Extreme Gradient Boosting surrogate optimized using the Covariance Matrix Adaptation Evolution Strategy achieves high predictive accuracy on unseen data (mean R 2 = 0.974). Global variance attribution is performed using a Sobol-inspired, permutation-based sensitivity measure designed for correlated inputs, enabling robust sensitivity analysis of black-box models without relying on the independence assumption. Cement, Water, and Age are identified as dominant contributors, with notable interaction effects between Cement–Water and Coarse aggregate–Superplasticizer. These findings are further supported by SHAP analysis and partial dependence plots. External validation using independently published experimental data and deployment in a web-accessible environment demonstrate the framework’s reliability and practical relevance for sustainable concrete mix design. Overall, the proposed framework offers a practical and interpretable tool for supporting robust and sustainability-oriented concrete mix design under correlated input conditions.