Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network

Abstract

Resilient modulus (Mr) of subgrade soils is considered as one of the most important factors for designing flexible pavements using empirical methods as well as mechanistic-empirical methods. The resilient modulus is commonly measured by a dynamic triaxial loading test, which is complex and expensive. In this research, back-propagation artificial neural network method has been employed to model the resilient modulus of clayey subgrade soils based on the results of the cone penetration test. The prediction of the resilient modulus of clayey subgrade soil can be possible through the developed neural network based on the parameters of the cone tip resistance (qc), sleeve friction (fs), moisture content (w), and dry density (γd). The results of the present study show that the coefficients of determination (R2) for training and testing sets are 0.9837 and 0.9757, respectively. According to the sensitivity analysis results, the moisture content is the least important parameter to predict the resilient modulus of clayey subgrade soils, while the importance of other parameters is almost the same. In this study, the effect of different parameters on the resilient modulus of clayey subgrade soil was evaluated using parametric analysis and it was found that with increasing the cone tip resistance (qc), the sleeve friction (fs) and the dry density (γd) and also with decreasing the moisture content (w) of soils, the resilient modulus of clayey subgrade soils increases.

Keywords

• resilient modulus
• clayey soils
• cone penetration test
• back-propagation algorithm
• artificial neural network