Abstract:

This article examines the performance of Minimax Probability Machine Regression (MPMR) for prediction of settlement(s) of shallow foundation on cohesionless soil. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. Width of footing (B), net applied pressure (q), average Standard Penetration Test (SPT) blow count (N), length (L), and embedment depth (Df) have been adopted as inputs of the MPMR. A sensitivity analysis has been carried out to determine the effect of each input. The results of MPMR have been compared with the Artificial Neural Network (ANN).