Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

Abstract

A commonly-encountered problem in foundation design is the reliable prediction of the pile bearing capacity (PBC). This study is planned to propose a feasible soft computing technique in this field i.e.; the Gaussian process regression (GPR) for the PBC estimation. The established database includes 296 number of dynamic pile load test in the field where the most influential factors on the PBC were selected as input variables. Several GPR models were designed and built. These models were assessed using three performance indices namely value account for (VAF), coefficient of determination (R2) and system error. To have a comparison, a genetic algorithm-based artificial neural network (GA-based ANN) model was also employed. It was found that the GPR-based model with VAF value of 86.41%, R2 of 0.84 and system error of 0.2006 is capable enough to predict the PBC and outperforms the GA-based ANN model. The results showed that the GPR can be utilized as a practical tool for the PBC estimation.