重庆大学学报2026，Vol.49Issue(5)：118-125,8.DOI:10.11835/j.issn.1000-582X.2026.05.009
基于机器学习的沥青路面国际平整度指数预测
Application of machine learning for predicting the IRI of asphalt pavements
摘要
Abstract
This study applies machine learning techniques to predict the international roughness index(IRI)of asphalt pavement using structural,performance,environmental,and traffic-related variables.Data were obtained from the long-term pavement performance(LTPP)database and Chinese pavement datasets,with 3 066 asphalt pavement sections(construction number=1)selected for analysis.Model parameters were optimized using cross-validation combined with grid search.Considering the selected factors,three machine learning models,namely artificial neural networks(ANN),support vector machines(SVM),and XGBoost,were employed to predict IRI.Their performance was evaluated using R²,root mean square error(RMSE)and mean absolute error(MAE).The results show that XGBoost achieved the best predictive performance(R²=0.96,RMSE=0.08,MAE=0.05).Feature importance analysis based on XGBoost indicates that the initial IRI is the most influential factor.These results show that XGBoost can accurately predict asphalt pavement IRI and provide a reference model for pavement management systems.关键词
机器学习/国际平整度指数/LTPP/多影响因素Key words
machine learning/IRI/LTPP/multiple factors分类
交通工程引用本文复制引用
付东雷,呙润华,王静怡..基于机器学习的沥青路面国际平整度指数预测[J].重庆大学学报,2026,49(5):118-125,8.基金项目
清华大学-丰田联合研究院跨学科专项(041911062).Supported by Tsinghua-Toyota Joint Research Institute Cross Discipline Program(041911062). （041911062）
