Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash

of 0.954 and a mean RMSE of 4.658 MPa, compared with 0.907 and 6.728 MPa for the ANN model. In addition, more than 60% of the ANN-BBO prediction errors fell within the range of [- 5%, 5%], whereas the corresponding proportion for the ANN model was about 39%, indicating more consistent predictive performance of the hybrid model. Interpretability analysis further indicated that curing age and water-to-binder ratio were the most influential variables governing compressive strength prediction. Overall, the results suggest that the ANN-BBO model may provide a useful tool for rapid strength estimation and preliminary evaluation of concrete containing POFA mixtures within the scope of the compiled dataset.