Towards Sustainable Construction: Hybrid Prediction Modeling for Compressive Strength of Rice Husk Ash Concrete

Rice husk ash (RHA) offers an eco-friendly way to improve concrete. Owing to the complex mix design of RHA concrete, accurately predicting its strength remains a challenge. This study addresses this need by compiling a dataset of 291 compressive strength records for RHA concrete. Using seven key input variables (e.g., cement, water, and RHA content), three novel hybrid models were developed by integrating the XGBoost algorithm with advanced metaheuristic optimizers: Northern Goshawk Optimization (NGO), Arctic Puffin Optimization (APO), and Catch Fish Optimization Algorithm (CFOA). These hybrid models were compared against classic Random Forest (RF), and Support Vector Regression (SVR), and unoptimized XGBoost models. The results demonstrated that all hybrid models significantly outperformed the unoptimized classic models. The APO–XGBoost model achieved the highest prediction accuracy on the testing set (RMSE = 3.5462, R2 = 0.9579 on testing set), followed by CFOA–XGBoost and NGO–XGBoost. Cement content was revealed to be the most influential parameter on compressive strength, as determined by a sensitivity analysis, ahead of both water and coarse aggregate content. This research confirms the superiority of metaheuristic-optimized hybrid models for predicting the strength of RHA concrete, providing a reliable data-driven tool to support its mix design and promote its application in sustainable construction.