Interpretable machine learning model for forecasting compressive strength of aeolian sand concrete

Abstract

This study integrates Bayesian optimization (BO) with the natural gradient boosting (NGBoost) algorithm to accurately predict aeolian sand concrete (ASC) compressive strength. The main results are summarized as follows. 1) The NGBoost model demonstrates high precision in predicting ASC compressive strength, achieving testing set metrics with a coefficient of determination of 0.945, mean squared error of 4.145 MPa², and root mean squared error of 2.036 MPa. 2) Feature importance ranking from the NGBoost model identifies age as the significant factor influencing ASC compressive strength, while the effects of aeolian sand ratio, water-to-binder ratio (W/B), and coarse aggregate are minimal. 3) SHapley Additive exPlanations (SHAP) analysis indicates a positive correlation between age, cement, coarse aggregate, superplasticizer, and the compressive strength of ASC. In contrast, the aeolian sand ratio, W/B, and fine aggregate show negative correlations. 4) A python-based graphical user interface (GUI) has been developed to enable engineers to predict ASC compressive strength efficiently, thus enhancing the model’s practical application.

Graphical abstract

Keywords

ASC / compressive strength / NGBoost / BO / SHAP / GUI

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