Explainable machine learning-based prediction model for dynamic resilient modulus of subgrade soils

Abstract

The dynamic resilient modulus (M_R) of a subgrade soil is a fundamental parameter for evaluating the dynamic stability and service resilience of subgrade fillers and structures, as well as an instrumental input for calculating the mechanical response and fatigue life of a pavement structure. To accurately and reasonably characterise the M_R of subgrade soils, machine learning (ML) models were established using the support vector machine, random forest, and extreme gradient boosting algorithms based on a large-scale dataset including 3533 records of M_R tests conducted on subgrade soils. Meanwhile, the weighted plasticity index (WPI), initial moisture content (w), dry unit weight (γ_d), confining stress (σ_c), deviator stress (σ_d), and numbers of freeze–thaw cycles (N_FT) were set as the input variables to predict the M_R using ML models, which considered the effects of wheel loads, physical properties and climate fluctuation on the subgrade soils during the service period. Subsequently, the Shapley additive explanations method was developed to explain the prediction model for the M_R of subgrade soils based on ML algorithms. The results quantitatively illustrated the explicit mapping relationship and internal influencing mechanism between the significant features of the influences and M_R of subgrade soils, which was consistent with prior experimental and physical cognition. In summary, the study findings provide meaningful guidelines for the structural design and life evaluation of pavement subgrade engineering.