Prediction of Stone Column Bearing Capacity Using Artificial Neural Network Model (ANNs)

Abstract

In the area of ground improvement the stone columns (SCs) play a definite role. The ground treatment technique has demonstrated to be effective in improving the embankments stability and natural slopes by rising the bearing capacity and decreasing settlements. The objectives of this study are to develop models for predicting the performance of SCs supported embankment foundation utilizing artificial neural network (ANN). For the aim of creating ANN models, training; testing and validation set comprising 70%, 15%, and 15% of the data, respectively steps were done, making use of available numerical results were obtained from the 2D finite element analysis. A dataset including of about 200 cases is involved and the mean square error (MSE) with R-squared value are used as performance metrics of the system. The applied data in ANN models are arranged in component of 4 input parameters which cover column diameter d, center to center spacing S, the internal friction angle of columns material ϕ, and embankment high H. Relating to these input parameters, the selected responses were; the bearing capacity of the SC (BC) and safety factor against the stability (SF). Based on the simulated results, an ideal 4-14-1 ANN architecture has been settled for the direct prediction. According to the technique was used, the forecasted data from the model had a good agreement with the actual datum, where the high regression coefficient (R2) was equals to 0.995 and 0.891 for BC and SF models, respectively. Furthermore, the relative importance of influential variables are examined, which shows that the column diameter is the most effective parameter in two study models with an significance score of 32.9%. Finally, the outcomes clearly demonstrated that the ANN method is reliable for modelling and optimizing of the SC behaviour.