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Fatigue cracking prediction of RIOHTrack full-scale test track based on variable-order fractional Burgers model

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/persons/resource/yu.wang

Wang,  Yu
Potsdam Institute for Climate Impact Research;

Cao,  Jinde
External Organizations;

/persons/resource/zhen.su

Su,  Zhen
Potsdam Institute for Climate Impact Research;

Huang,  Wei
External Organizations;

Abdel-Aty,  Mahmoud
External Organizations;

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Zitation

Wang, Y., Cao, J., Su, Z., Huang, W., Abdel-Aty, M. (2024): Fatigue cracking prediction of RIOHTrack full-scale test track based on variable-order fractional Burgers model. - Engineering Fracture Mechanics, 307, 110330.
https://doi.org/10.1016/j.engfracmech.2024.110330


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_30445
Zusammenfassung
We propose a modified thermal cracking (MTC) model in this paper, which utilizes the evolution of crack propagation to predict the number and density of cracks that will form on the RIOHTrack full-scale test track. We incorporate the variable-order fractional Burgers model as an important component of the thermal cracking (TC) model, for calculating the creep compliance. Furthermore, we apply the Levenberg–Marquardt (LM) algorithm as a data-driven approach, for parameter fitting. The MTC model is constructed technically through creep compliance approximation compute and parameter fitting as follows. First, we employ the variable-order fractional Burgers model to calculate the asymptotic creep compliance of each pavement segment within the RIOHTrack full-scale test track. More precisely, we derive the explicit asymptotic expansion of creep compliance based on the variable-order fractional Burgers model. Such an asymptotic expansion represented by the function is a simplified representation of the creep compliance, and it ensures that the fitting accuracy is greater than 0.87. Parameter fitting of the creep compliance is based on the RIOHTrack full-scale pavement test data, using the LM algorithm. Then, the creep compliance in the original TC model is mathematically upgraded to the above variable-order fractional approximation form, and parameters in the MTC model are numerically estimated using the LM algorithm based on prior knowledge from the RIOHTrack test data. Our prediction analyses based on the RIOHTrack data demonstrate the superior performance of the proposed MTC model.