Prediction of California bearing ratio and modified proctor parameters using deep neural networks and multiple linear regression: A case study of granular soils

Abstract

The California Bearing Ratio (CBR) and modified proctor parameters belong to the soil geotechnical properties used to assess soil behavior. Direct measurement of these properties can be quite time-consuming in large-scale applications or when immediate results are required. Therefore, significant research efforts have been made in the literature to develop indirect methods for their estimation. However, some gaps in the state-of-the-art can be highlighted in these topics, such as the deficiency in computational models to calculate the maximum dry unit weight (γd(max)), optimum moisture content (wopt) and CBR, and the lack of methods that consider their intrinsic influence on each other. Hence, in this investigation, mathematical and computational models were created to obtain the above-mentioned variables from the soil grain size distribution. The mathematical model was based on Multiple Linear Regression (MLR) correlations. Meanwhile, the computational model was constructed from a custom-made Deep Neural Networks (DNNs) architecture. Subsequently, the accuracy of these models was validated with an experimental case study. The results demonstrated that the proposed methods in this study are more precise than previous approaches in the literature. Accordingly, the main contribution of this manuscript to the industry is the formation of models with high exactness to predict the γd(max), wopt and CBR of granular soils.

Keywords

• California bearing ratio
• Granular soils
• Modified proctor test
• Neural networks