Predicting the compressive strength of eco-friendly concrete incorporating natural pozzolans: A hybrid machine learning modeling with SHAP and PDP analyses

The construction and materials sector is actively striving to mitigate the environmental consequences of cement production in concrete by integrating alternative and supplemental cementitious materials while reducing carbon emissions. Because of their pozzolanic reactions, natural pozzolans (NPs) have become prominent in this area. The aim of this research is to accurately predict the compressive strength of normal-weight concrete that contains NP by investigating the impact of several elements, including cement, NP content, water and aggregate quantity, and superplasticizer content. For doing this, the research examined data gathered from various sources, which led to the creation of a dataset consisting of 496 mix ratios with strengths. A comprehensive analysis was conducted using numerous advanced machine learning (ML) algorithms, including extreme gradient boosting (XGB), adaptive boosting (ADB), and bagging regressor (BAG), as well as hybrid ML techniques such as XGB-ADB and XGB-BAG. The purpose was to extensively examine the concrete mix materials and evaluate their influence on strength. The collected dataset was divided into two groups: training and testing. Statistical tests were conducted to ascertain the correlations between the input parameters and strength. Furthermore, the algorithms' performance was assessed using four separate statistical assessment criteria. The hybrid XGB-BAG model exhibited superior accuracy (test R-2 = 0.901) in comparison to other models. All other models also demonstrate adequate performance (R-2 greater than 0.80) for the use of predicting the compressive strength of NP-concrete. In addition, the SHapley Additive Explanations (SHAP) study indicated that cement, NPs, and superplasticizers had a beneficial impact on strength. In summary, the research indicates that the hybrid XGBADB model, when combined with the indicated input parameters, can effectively forecast the compressive strength of NP- concrete.