An interpretation framework of machine learning models: prediction of pavement long-term performance

Poor interpretation of machine learning models restrains its extensive application to practical engineering. In this regard, this study proposed an interpretation framework of machine learning models used for the prediction of pavement long-term performance, which consists of feature importance based on the SHapley Additive exPlanations (SHAP) method, factor influence analysis based on Accumulated Local Effects (ALE) and Local Conditional Effects, and feature interaction analysis based on H-statistic and second-order ALE. The interpretation framework was validated through its application in the prediction of pavement roughness and rutting. The Gradient Boost Regression Trees model was used as the machine learning model and the generalisation performance of the model exceeded 80%. According to the proposed interpretation framework, it can be concluded that environment-related factors dominated the deterioration of pavement roughness. The influence of precipitation on the pavement roughness followed a logarithmic increase. There was a threshold effect for the influence of traffic load on pavement roughness. In terms of pavement rutting, the traffic load and pavement structure number were the determinants. Among environmental factors, short-wave radiation contributed the most to pavement rutting. Feature interaction was related to the feature level and tended to be more significant at a relatively low feature level.