Empirical approach for bearing capacity prediction of geogrid-reinforced sand over vertically encased stone columns floating in soft clay using support vector regression

Due to the complex, elaborate and expensive estimation of bearing capacity (q_rs) of geogrid-reinforced sand bed resting over a group of vertically encased stone columns floating in soft clay, it is required to develop a precise empirical model, which is supposed to be nonlinear. To date, there is no established bearing capacity equation available on this topic. The aim of this work is to develop a precise q_rs prediction model using support vector regression (SVR) technique. A total of 245 experimental datasets were collected and used to train and test the SVM models estimating the q_rs. Three SVR models were developed based on three different kernel functions, namely exponential radial basis kernel function (ERBF), radial basis kernel function (RBF) and polynomial kernel function (POLY), and their performances were examined. Out of the three SVR models, one with ERBF was found to be the best one, having the lowest statistical error and maximum generalization ability of the training and testing data. The performance of SVR-ERBF model was compared with adaptive neuro-fuzzy inference system (ANFIS) model, and it was observed that SVR-ERBF model outperforms ANFIS model to predict q_rs. A sensitivity analysis was also conducted to identify the relative importance and contribution of each input variable on output (q_rs) prediction. Finally, using the SVR-ERBF model, an empirical equation is proposed to predict q_rs for practical application purposes. Obtained results approve that the SVR-ERBF model can be used as a powerful and reliable alternative to solve highly nonlinear problems such as indirect estimation of q_rs.