基于机器学习的沥青路面国际平整度指数预测OA

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.