Affiliation:
1. Faculty of Civil Engineering and Geosciences Delft University of Technology South Holland The Netherlands
Abstract
AbstractThe relationship between real‐world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.
Funder
Ministry of Infrastructure and Water Management
Rijkswaterstaat
Reference65 articles.
1. Abouelsaad A. &White G.(2020).Fretting and ravelling of asphalt surfaces for airport pavements: A load or environmental distress. InProceedings of the 19th annual international conference on highways and airport pavement engineering asphalt technology and infrastructure(pp.11–12).Liverpool John Moores University Liverpool UK.https://research.usc.edu.au/esploro/outputs/conferencePresentation/Fretting‐and‐ravelling‐of‐asphalt‐surfaces/99575308702621
2. Review of Asphalt Mixture Ravelling Mechanisms, Causes and Testing
3. Grouping Pavement Condition Variables for Performance Modeling Using Self‐Organizing Maps
4. A method for determining the appropriate frequency for testing asphalt mixtures in the laboratory
5. Cai W. Song A. Du Y. Liu C. Wu D. &Li F.(2023).Fine‐grained pavement performance prediction based on causal‐temporal graph convolution networks.IEEE Transactions on Intelligent Transportation Systems 1–14.https://ieeexplore.ieee.org/abstract/document/10311071