The application of deep neural networks for the prediction of California Bearing Ratio of road subgrade soil

Abstract

California Bearing Ratio (CBR) of the subgrade soil is one of the essential values for the design and construction of the asphalt pavement of highway projects. However, the estimation of the CBR value through laboratory tests is time-consuming and labor-intensive. Thus, this study focuses on providing artificial neural network (ANN) prediction models that can be efficiently used for the prediction of the CBR value of the subgrade soil in Egypt from the grain size distribution, Atterberg limits, and compaction parameters. 240 ANNs with different hyperparameters are investigated in order to optimize the hyperparameters so that the final chosen ANN can provide accurate results. The analysis shows that the deep neural networks outperform the shallow ANNs. Finally, comparing the performance of the ANNs with the traditional multiple linear regression (MLR) shows that ANNs outperform the MLR models as the ANNs have much better performance and can generate highly accurate predictions.