A new FCM-XGBoost system for predicting Pavement Condition Index

•We comprehensively analyze various factors affecting pavement condition index (PCI)•We identify the important determinant features for building PCI prediction models.•We develop a new FCM-XGBoost modelling framework to effectively predict the PCI. Predicting Pavement Condition Index (PCI) is crucial for identifying potential pavement distresses and developing effective pavement maintenance strategies. Here, a new Fuzzy C-Means-eXtreme Gradient Boosting (FCM-XGBoost) PCI prediction system is developed. First, asphalt pavement sections and concrete pavement sections are distinguished, and the Fuzzy C-Means (FCM) clustering algorithm is employed to cluster the asphalt and concrete expressway sections respectively based on multiple factors influencing PCI. Next, an eXtreme Gradient Boosting (XGBoost) model is trained for each cluster of expressway sections. Finally, in the system application stage, the PCI of an expressway section for the next year is predicted using the XGBoost model trained for the specific cluster that the expressway section belongs to. Validation results based on actual pavement condition data indicate that the proposed FCM-XGBoost PCI prediction system can achieve slightly higher accuracy compared with several high performance benchmark models, which include the Random Forest model, the Gradient Boosting Decision Tree model, the Light Gradient Boosting Machine model and the XGBoost model. The developed PCI prediction system contributes to the improvement of PCI prediction approaches and can be potentially applied in deploying effective and cost-efficient pavement maintenance strategies.