Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip Footing Subjected to Eccentric Inclined Load and Resting on Sand

Abstract

The present study attempts to predict the ultimate bearing capacity (UBC) of the strip footing resting on sand and subjected to inclined load having eccentricity with respect to the vertical using three different soft computing techniques such as support vector mechanism with radial basis function (SVM RBF kernel), M5P model tree (M5P) and random forest regression (RFR). The UBC was computed in the form of reduction factor and this reduction factor was assumed to be dependent on the ultimate bearing capacity (qu) of the strip footing subjected to vertical load, eccentricity ratio (e/B), inclination ratio (α/ϕ) and the embedment ratio (Df/B). The performance of each model was analyzed by comparing the statistical performance measure parameters. The outcome of present study suggests that SVM RBF kernel predicts the reduction factor with least error followed by M5P and RFR. All the model predictions further outperformed those based on semi-empirical approach available in literature. Finally, sensitivity analysis performed for the SVM RBF kernel model which suggests that the inclination ratio (α/ϕ) and eccentricity ratio (e/B) was an important parameter, in comparison to other parameters, considered for predicting the reduction factor.

Keywords

• ultimate bearing capacity
• reduction factor
• eccentrically inclined load
• svm rbf kernel
• m5p model tree
• random forest regression