Using an evolutionary heterogeneous ensemble of artificial neural network and multivariate adaptive regression splines to predict bearing capacity in axial piles

•The study integrates the equilibrium optimization (EO) with the heterogeneous ensemble of MARS and RBFNN, abbreviated as IMNNIM.•The IMNNIM’s performance is validated on 472 test reports of the driven pile static load collected on the construction site.•An extensive analysis was conducted to remove the redundant attributes.•The analytical results demonstrated IMNNIM to be the best model in predicting PBC by achieving the greatest values of MAPE (7.24%), RMSE (90.92kN), MAE (67.98kN) and R2 (0.930).•A one-tailed t-test has proven the prediction accuracy of IMNNIM to be significantly superior to that of other approaches with the 95% confidence.