An improved regression model to predict fatigue life of asphalt mixes incorporating low to moderate RAP contents

The research on the fatigue performance of recycled asphalt mixture and the establishment of its fatigue life prediction model is the key to its large-scale engineering application. The four-point bending fatigue tests of the fresh and recycled asphalt mixtures under the strain control of cyclic loading were conducted to investigate influencing factors on their fatigue performance and establish a new fatigue prediction model for RAP (recycled asphalt pavement). The hot recycled asphalt mixture samples of AC-16C were prepared by a uniform test design method, with the RAP content of 0%, 20%, 30%, and 40%. Through the fatigue test data of 20 groups of fresh asphalt mixture with 0% RAP and 40 groups of recycled asphalt mixtures with the contents of 20%, 30%, and 40% RAP, the simplified applicability of the fatigue prediction model in the current Chinese specifications for the design of highway asphalt pavement (JTG D50-2017, JTG model) and its parameters modification were studied, thereby an improved regression fatigue prediction model of recycled asphalt mixture is established on consideration of strain level, asphalt content, void ratio, and RAP mixing amount. This fatigue prediction model for recycled asphalt mixtures was proposed by modifying the parameters of the fatigue equation for new asphalt mixtures in the Chinese Highway Asphalt Pavement Design Code. (JTG D50-2017) and comparing the fatigue test data of the recycled asphalt mixture. The research results show that: The simplified JTG fatigue prediction model can effectively predict the fatigue life of fresh asphalt mixture with the content of 0% RAP, and the model parameter a is a constant of the order of 1016; The correction coefficients α and β of the initial bending stiffness modulus S0 and the Voids Filled with Asphalt (VFA) in the improved JTG model of the hot recycled asphalt mixture considering the RAP content are 0.006 and 0.0136, respectively; The model can accurately predict the fatigue life of recycled asphalt mixtures with the range of RAP content of 0–40%, with an average deviation of only 0.106, and has higher prediction accuracy for fatigue life measured close to or higher than 106 times. The average deviation of the predicted life of 20%, 30%, and 40 %RAP fatigue is only 0.057, 0.079, and 0.125. This paper’s indoor fatigue prediction model has higher accuracy for recycled asphalt mixture than the well-known traditional models.

• Record URL:
  https://doi.org/10.1016/j.conbuildmat.2023.130904
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0950061823006165
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Zhichao
  • 0000-0003-1379-6522
  • Liu, Donghai
  • 0000-0001-6895-9646
  • Hu, Bin
  • Zhu, Chongzheng
  • Luo, Wenbo
  • 0000-0002-3528-2111
• Publication Date: 2023-4-17

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Illustrations; References; Tables;
• Pagination: 130904
• Serial:
  • Construction and Building Materials
  • Volume: 374
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0950-0618
  • Serial URL: http://www.sciencedirect.com/science/journal/09500618?sdc=1

Subject/Index Terms

• TRT Terms: Asphalt mixtures; Bend tests; Fatigue tests; Predictive models; Reclaimed asphalt pavements; Regression analysis
• Geographic Terms: China
• Subject Areas: Highways; Materials; Pavements;

Filing Info

• Accession Number: 01877981
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2023 9:32AM