Artificial Neural Network Modeling For California Bearing Ratio (CBR) Prediction in Clayey Soil Stabilised with Tyre Buffings and Lime

Abstract

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the California Bearing Ratio (CBR) values of clayey soil stabilised with tyre buffings and lime. To achieve this, a series of the CBR tests were performed on clayey soil mixed with tyre buffings and lime in ratios of 0, 5, 10, and 15%, and 0, 2, 4, and 6% of dry weight of the specimens, respectively. The results of the CBR tests were used in the development of both models. The predicted CBR values from both ANN and MR models were compared with experimentally measured CBR values. It is found that the ANN model gives more reliable predictions than the MR model. Moreover, several performance indices called as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were used as the criteria for measuring the performance of the ANN and MR models. Based on these indices, the proposed ANN model yielded better performance than MR model in predicting the CBR values of clayey soil stabilized with tyre buffings and lime.