A Unified Framework for Asphalt Pavement Distress Evaluations Based on an Extreme Gradient Boosting Approach

Flexible pavements are susceptible to distress when subjected to long-term vehicle loads and environmental factors, thereby reqsuiring appropriate maintenance. To overcome the hectic field data collection and traffic congestion problems, this paper presents an intelligent prediction system framework utilizing Extreme Gradient Boosting (XGboost) to predict two relevant functional indices: rutting deformation and cracks damage. The model framework considers multiple essential factors, such as traffic load, material characteristics, and climate data conditions, to predict rutting behavior and employs image data to classify cracks behavior. The Extreme Gradient Boosting (XGboost) algorithm exhibited good performance, achieving an R2 value of 0.9 for rutting behavior and an accuracy of 0.91, precision of 0.92, recall of 0.9, and F1-score of 0.91 for cracks. Moreover, a comparative assessment of the framework model with prominent AI methodologies reveals that the XGboost model outperforms support vector machine (SVM), decision tree (DT), random forest (RF), and K-Nearest Neighbor (KNN) methods in terms of quality of the result. For rutting behavior, a SHAP (Shapley Additive Explanations) analysis was performed on the XGboost model to interpret results and analyze the importance of individual features. The analysis revealed that parameters related to load and environmental conditions significantly influence the model's predictions. Finally, the proposed model provides more precise estimates of pavement performance, which can assist in optimizing budget allocations for road authorities and providing dependable guidance for pavement maintenance.