Concrete strength prediction is of great relevance for construction safety and quality assurance; however, these methods often trade-off their accuracy or interpretability, especially when it comes to the use of supplementary cementitious materials like fly ash in process. This study aims to build an interpretable, highly accurate model for predicting the compressive and tensile strength of concrete with a hybrid approach based on gradient boosting (XGBoost), deep neural networks (DNNs), and optimization via AutoGluon Process. The model is put into a multitask learning (MTL) framework that includes mix design variables, environmental factors, and non-destructive testing (NDT) data samples. The interpretation of model predictions is accomplished through SHAP and LIME to quantify global and local importance. Results show an impressive R² score of 0.91 on the test set with a 23% reduction in MSE and LIME fidelity exceeding 0.87. This shows a 10-15% increase in the mean-squared error, surpassing existing models. Feature analysis shows that fly ash percentage contributes around 25% to the predictions. The proposed solution thus offers a robust interpretability platform for concrete strength prediction and further shows great promise for optimization in material design and structural integrity assurances. This work serves as a landmark in bridging the gap between hybrid modeling with automated optimization and explainability for concrete strength predictions.
Nandurkar et al. (Thu,) studied this question.