Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques

This study investigates the applicability of two artificial intelligence (AI) techniques, namely, the support vector regression (SVR) and artificial neural network (ANN), in prediction of the bearing pressure of spread footings on clayey soils based on plate load test (PLT) data. A data set consisting of 576 numbers of data points from 58 numbers of full-scale and small-scale PLTs was collected from the literature. 70% of the data were used for training and the remaining 30% of data were used to test the AI models. The data division was checked with adequate statistical tests. A correlation analysis was performed to optimize the number of inputs. Three SVR models, namely, the polynomial kernel function (POLY), radial basis kernel function (RBF), and exponential radial basis kernel function (ERBF), and one ANN model with the Bayesian Regularization (BR) learning algorithm were used in the analysis. The model performances were evaluated by comparing various error parameters. The ANN-BR model showed the best performance with minimum error and a correlation co-efficient (R) of 0.9851 for the overall dataset. A sensitivity analysis indicates that the undrained cohesion has a maximum influence on the bearing pressure. Finally, an empirical approach is presented using the best-performing model to determine the soil bearing pressure.