The Prediction of Swell Percent and Swell Pressure by Using Neural Networks

Abstract

Expansive soils exhibit significantly high volumetric deformations and so pose a serious threat to stability of the structures and foundations. Thus, determination of their swelling properties (i.e. swelling potential and swell pressure) becomes essential. However, measurement of the swelling properties is time-consuming and requires special and expensive equipment. With this in view, efforts were made to develop artificial neural network (ANN) and multiple regression analysis (MRA) models that can be employed for estimating swell percent and swell pressure. To achieve this, the results of free swell tests performed on statically compacted specimens of Kaolinite-Bentonite clay mixtures with varying soil properties were used. Two different ANN (ANN-1 and ANN-2) and MRA (MRA-1 and MRA-2) models have been developed: ANN-1 and MRA-1 models for predicting swell percent and ANN-2 and MRA-2 models for predicting swell pressure. The results obtained from ANN and MRA models were compared vis-à-vis those obtained from the experiments. The values predicted from the ANN models match the experimental values much better than those obtained from MRA models. Moreover, several performance indices such as determination coefficient (R2), variance account for (VAF), mean absolute error (MAE), and root mean square error (RMSE) were calculated to check the prediction capacity of the ANN and MRA models developed. The obtained indices make it clear that the constructed ANN models have shown higher prediction performance than MRA models. It has been demonstrated that the ANN models can be used satisfactorily to predict swell percent and swell pressure as a rapid inexpensive substitute for laboratory techniques.