Failure modes of asphalt pavement with top-down cracks based on measured aging gradients in field cores

The modulus gradient generated by uneven aging in the field might be a significant factor for top-down cracks (TDCs), but it has not been adequately or precisely considered to date, which affects the accuracy of TDC analysis and prediction. The purpose of this study is to investigate the aging gradient of field asphalt mixtures and to analyze the failure mode of TDCs in the context of aging gradients. Therefore, nanoindentation tests were carried out on field cores to explore the distribution of aging gradients. Subsequently, the influence of traffic loads, structural layer moduli, and modulus gradients on TDC was analyzed using the finite element method (FEM). Finally, by importing the measured field core data, an extended finite element method (XFEM) model was established to investigate the failure mode and fatigue life of TDC and the effects of field aging. The results confirm the existence of significant aging gradients in the asphalt layers along depth and indicate that the increase in traffic loads, upper layer moduli, and modulus gradients accelerates TDC propagation, while the modulus growth of the middle and bottom layers and base can make it slow down. The fatigue life of asphalt layers with aging gradients decreases by 51.45 % compared to those disregarding long-term aging, which suggests that it is necessary to consider the effects of uneven aging gradient in TDC analysis and pavement design. The findings of this paper will provide guidance for the analysis and prevention of TDCs and the structural design of long-life pavements.

• Record URL:
  • https://doi.org/10.1016/j.conbuildmat.2024.137050
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0950061824021925
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Fan, Yulou
  • 0000-0002-6457-1764
  • Yu, Yunhong
  • Shi, Chenguang
  • Wu, You
  • 0000-0001-9807-1832
  • Huang, Siqi
  • Zhou, Yixin
  • Wang, Houzhi
  • Yang, Jun
  • 0000-0001-8407-8427
  • Huang, Wei
• Publication Date: 2024-8-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 137050
• Serial:
  • Construction and Building Materials
  • Volume: 438
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0950-0618
  • Serial URL: http://www.sciencedirect.com/science/journal/09500618?sdc=1

Subject/Index Terms

• TRT Terms: Aging (Materials); Asphalt pavements; Core samples; Cracking; Failure
• Subject Areas: Highways; Maintenance and Preservation; Materials; Pavements;

Filing Info

• Accession Number: 01925589
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 26 2024 10:00AM