A Deep Learning Approach for Modelling of Resilient Modulus of Compacted Subgrade Subjected to Freezing-Thaw Cycles and Moistures

This study employs a deep learning approach to determine the resilient modulus of compacted subgrade, which is one of the most important stiffness characteristics in pavement design. The proposed paradigm, i.e., deep neural network (DNN), comes under the category of artificial neural network with several hidden layers and activation functions. A total of 2813 data of subgrade soils, comprising six influencing parameters namely weighted plasticity index, dry unit weight, confining stress, deviator stress, moisture content, and the number of freezing-thaw cycles, were considered for the creation and validation of the model. The results of the employed DNN were compared with those of other benchmark techniques, such as feed-forward neural network, k-nearest neighbour regressor, extreme learning machine, random forests regressor, multivariate adaptive regression spline, and multiple linear regression. As per the determination coefficient (R2) and root mean square error (RMSE) indices, the developed DNN achieved the maximum degree of precision of robust modulus during both training (R2 = 0.9947 and RMSE = 0.0094) and testing (R2 = 0.9797 and RMSE = 0.0183) phases. The study also employed DNN-based monotonicity analysis to examine the effects of different influencing parameters. Overall, the developed DNN has demonstrated the potential to assist geotechnical and geological engineers in estimating the resilient modulus of compacted subgrade at varying freezing-thaw cycles and moistures during the preliminary phase of the engineering projects. The developed Python code is attached for future research.