Abstract

This study focuses on accurately predicting rutting depth (RD) through a probabilistic prediction model, which is crucial for timely road maintenance and traffic safety. Natural Gradient Boosting (NGBoost) technology is employed to achieve this goal as it provides accurate predictions and estimates the uncertainty of predictions. Furthermore, to explain the decision-making process of the proposed prediction model, Shapley Additive Explanations (SHAP) analysis is applied to identify and quantify significant factors affecting RD. Research results show that the NGBoost model surpasses the traditional Gaussian Process and Random Forest model regarding prediction accuracy and performs better regarding certainty of prediction intervals. Meanwhile, SHAP analysis improves the transparency of model decision-making and further validates the model. Explanatory ability elucidates the impact of other characteristics on RD. The two-stage probabilistic prediction model not only enhances the reliability of predictions but also provides an in-depth understanding of the key influencing factors of pavement performance, improving the efficiency and effectiveness of maintenance measures.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.