Bearing capacity prediction of strip and ring footings embedded in layered sand

Abstract

A prediction model for the bearing capacity estimation of strip and ring footing embedded in layered sand is proposed using soft computing approaches, namely, artificial neural network (ANN) and random forest regression (RFR). The required data for the model preparation were generated by performing lower- and upper-bound finite-elements limit analysis by varying the properties of the top and bottom layers. Two types of layered sand conditions are considered in the study: (a) dense on loose sand; (b) loose on dense sand. The investigation for strip footing was carried out by varying the thickness of the top layer, embedment depth of the foundation and friction angles of top and bottom layers. For a ring footing, the internal-to-external diameter ratio forms an additional variable. In total, 1222 and 4204 data sets were generated for strip and ring footings, respectively. The performance measures obtained during the training and testing phase suggest that the RFR model outperforms the ANN. Also, following the literature, an analytical model was developed to predict the bearing capacity of strip footing on layered sand. The ANN and the generated analytical model predictions agreed with the published experimental data in the literature.