Rutting prediction model of asphalt pavement based on RIOHTrack full-scale ring road
The research and prediction of rutting mechanism of asphalt pavement has always been the main difficult problem for road designers all over the world, and scholars have done a lot of research on how to prevent and control rutting of asphalt pavement. Based on the measured loading data of 80 million...
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Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2025-01, Vol.242, p.115915, Article 115915 |
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Sprache: | eng |
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Zusammenfassung: | The research and prediction of rutting mechanism of asphalt pavement has always been the main difficult problem for road designers all over the world, and scholars have done a lot of research on how to prevent and control rutting of asphalt pavement. Based on the measured loading data of 80 million axis times of Beijing RIOHTrack full-scale ring road, rutting evolution of asphalt pavement with various structure types is studied in this paper and proposes the Kou&Cao rutting prediction model for the first time. Compared with other mature explicit rutting prediction models, this model has the characteristics of high prediction accuracy, strong robustness. At the same time, the model can provide a basis for predicting the inflection point of rutting evolution on asphalt pavement. The research results of this model are effective, which completes the effective analysis of the rutting evolution and development law of asphalt pavement, and provides an important reference for the future road design specification research and road construction.
•This model is an accurate fitting of the temporal and spatial evolution of rutting.•It is applicable to all seven groups of asphalt pavement with different structures.•The explicit model is helpful to the prediction of rutting inflection point.•This model, named Kou&Cao, was proposed based on the data of 80 million axle loads.•Model structure is regular and concise, has basic condition for further expansion. |
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ISSN: | 0263-2241 |
DOI: | 10.1016/j.measurement.2024.115915 |