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

• Published in: Transportation Geotechnics Vol. 49; p. 101415
• Main Authors: Li, Xiangyang, Liu, Wenjun, Xu, Changjing, Liu, Ning, Feng, Shuaike, Zhang, Xin, Li, Yanbin, Hao, Jianwen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Dynamic resilient modulus; Machine learning; Prediction model; SHAP method; Subgrade soil; Subgrade soil; SHAP method; Dynamic resilient modulus; Machine learning; Prediction model
• ISSN: 2214-3912; 2214-3912
• DOI: 10.1016/j.trgeo.2024.101415

The dynamic resilient modulus (MR) 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 MR 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 MR tests conducted on subgrade soils. Meanwhile, the weightedplasticity index (WPI), initial moisture content (w), dry unit weight (γd), confining stress (σc), deviator stress (σd), and numbers of freeze–thaw cycles (NFT) were set as the input variables to predict the MR 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 MR 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 MR 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.