Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters.