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Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track

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Abstract

Rutting deformation is typical damage to asphalt pavement and can be quantitatively described as the time-varying mechanical deformation feature of the asphalt mixture during the creep process. First, a nonlinear viscoelastic creep model is established based on the Caputo variable-order fractional derivative. The fractional order in this model is not fixed and is defined as a time-varying function. The time-dependent mechanical properties of asphalt mixtures are effectively reflected. Based on the modified Burgers model, the variable-order function is revised to a linear form. Second, the variable-order fractional Burgers rutting model is obtained by fitting the test data of the RIOHTrack full-scale track. The Levenberg-Marquardt optimization algorithm is used to analyze the test data and the corresponding parameters to obtain more accurate and applicable variable-order fractional rutting mechanics and empirical models for different asphalt pavement structures. Finally, according to the classification of the pavement structure, structures III and VI are taken as examples. The rutting prediction and comparison results of several representative pavement structures are obtained to verify that the proposed rutting prediction model can also accurately predict all road sections of RIOHTrack. The new model is also compared with the rutting model of the mechanistic-empirical pavement design guide, the fractional-order Burgers rutting model, and the modified Burgers rutting model. The fitting results are evaluated using the determination coefficient R2. The results show that the variable-order fractional Burgers rutting model proposed in this study exhibits a better fit and is more applicable to describe the rutting deformation of asphalt pavement.

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Acknowledgements

This work was supported in part by National Key Research & Development Project of China (Grant No. 2020YFA0714301) and National Natural Science Foundation of China (Grant No. 61833005).

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Authors and Affiliations

  1. School of Mathematics, Southeast University, Nanjing, 210096, China

    Yu Wang, Jiaojiao Yan & Jinde Cao

  2. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Southeast University, Nanjing, 210096, China

    Yu Wang, Jiaojiao Yan & Jinde Cao

  3. Intelligent Transportation System Research Center, Southeast University, Nanjing, 210096, China

    Wei Huang

  4. Systems Research Institute of the Polish Academy of Sciences, Warsaw, 01-447, Poland

    Leszek Rutkowski

  5. Institute of Computer Science, AGH University of Science and Technology, Krakow, 30-059, Poland

    Leszek Rutkowski

  6. Information Technology Institute, University of Social Sciences, Lodz, 90-113, Poland

    Leszek Rutkowski

  7. Yonsei Frontier Lab, Yonsei University, Seoul, 03722, Republic of Korea

    Jinde Cao

Authors
  1. Yu Wang
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  2. Jiaojiao Yan
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  3. Wei Huang
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  4. Leszek Rutkowski
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  5. Jinde Cao
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Correspondence to Jinde Cao.

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Wang, Y., Yan, J., Huang, W. et al. Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track. Sci. China Inf. Sci. 66, 152205 (2023). https://doi.org/10.1007/s11432-022-3647-7

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  • DOI: https://doi.org/10.1007/s11432-022-3647-7

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