YUAN J, LI J, JIAO H C, et al． Stress Prediction and Parameter Importance Analysis of Critical Load Position of Rigid Pavement Based on Machine Learning [J]. Journal of Henan Polytechnic University( Natural Science) ，doi: 10.16186/j.cnki.1673-9787.2025020025.

doi: 10.16186/j.cnki.1673-9787.2025020025

Received：2025-02-23

Revised：2025-04-21

Online：2025-04-22

Stress prediction and parameter importance analysis of critical load position of rigid pavement based on machine learning (Online)

YUAN Jie, LI Jie, JIAO Hua-cheng, JIA Xiang-yang

The Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, Tongji University, Shanghai 201804, China

Abstract: Objectives In order to quickly predict the critical load stress of different rigid pavement structures under various aircraft loads, the prediction of critical load stress and the importance of its parameters were studied based on machine learning method., Methods In this study, the critical load stress under different working conditions was calculated by establishing a three-dimensional finite element model of rigid pavement stress response, and the critical load stress database was constructed by collecting the critical load stress under all working conditions. Four machine learning algorithms, support vector machine (SVM), convolutional neural network (CNN), BP neural network and random forest (RF), were used to construct the stress prediction model. The critical load position stress data (2916 sets) of common single-axle dual-wheel (B737-800), dual-axle dual-wheel (B747-400), and triple-axle dual-wheel (B777-300ER) configurations were selected. After data screening and optimization, a dataset (2806 sets) for model training was established. The hyperparameters were optimized by grid search and 5-fold cross validation. The performance of the model was quantified based on RMSE, MAE, MBE and R². The importance of input parameters is analyzed by SHAP method. Finally, the variation law of critical load stress with structural parameters of each surface is analyzed. Results BP neural network model has the best prediction effect, and its R², MAE, RMSE and MBE are 0.94246, 0.12092, 0.16424 and 0.00034, respectively. The surface thickness has the most significant effect on the stress, and the average absolute SHAP value is 49.24%. The critical load stress decreases with the increase of the thickness of the surface layer, the reaction modulus of the top surface of the foundation, the modulus of the base layer and the thickness, and finally the decreasing trend tends to be stable. Based on the stress distribution and variation in different structural parameters, the suitable value range of structural parameters in rigid pavement design is given. Conclusions The machine learning model can predict the critical load stress well, and can provide technical support for the calculation of rigid pavement stress and thickness design .

Key words: Pavement Engineering; Rigid Pavement; Critical Load Position Stress; Machine Learning; Parameter Importance