Estimation of California Bearing Ratio by Artificial Neural Network
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Abstract
Laboratory CBR test is laborious, time-consuming, and expensive, resulting in the development of predictive models by employing multiple linear regression analysis (MLRA) and an artificial neural network (ANN). In this study, a low coefficient of determination in the testing set was deserved from the MLRA in which the gravel, sand, plastic index, and optimum moisture content were used as independent variables. However, the suitably developed ANN model was Model 5 in which fine content, plastic index, optimum moisture content and maximum dry density were used as independent variables. The Model 5 had the relatively high coefficient of determination in the testing set. The predicted CBR values using the ANN are compared with the CBR predicted by MLRA. The results demonstrate that ANN is a beneficial way of predicting CBR values, as it outperforms the MLRA. Moreover, a new ANN based formula is proposed for predicting CBR values. The new ANN based formula is beneficial to a preliminary design in earthwork for budget restrictions and limited time.
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