PINN-AFP: A novel C-S curve estimation method for asphalt mixtures fatigue prediction based on physics-informed neural network

The accurate prediction of fatigue life of asphalt mixture is the key to the design of long-lasting durable pavement. Currently, a critical aspect influencing the accuracy of life prediction methods for asphalt mixtures, based on Viscoelastic Continuum Damage Mechanics (VECD), is the precision of the damage characteristic (C-S) curve. Within the VECD framework, the C-S curve of asphalt mixture is regarded as an intrinsic material property. However, it has been observed in engineering applications that the C-S curve of the material exhibits notable sensitivity to varying loading conditions. The nonlinear fitting method based on a small number of experiments is difficult to fully characterize the fatigue performance of the material, while a large number of complete material fatigue tests are expensive in time and money. Based on the above problems, a physics-informed neural network embedded in VECD, PINN-AFP, is proposed. It can accurately predict the complete material C-S curve based on a small amount of pre-fatigue data of the material, thus achieving accurate prediction of the fatigue life of asphalt mixture. The case study uses the fatigue test data of AC-25 as an example, and the results demonstrate that the proposed PINN-AFP has strong generalization ability and prediction accuracy, achieving the state-of-the-art in the mainstream machine learning and deep learning methods with an average 5.2% fatigue life prediction error.

• Record URL:
  • https://doi.org/10.1016/j.conbuildmat.2024.135070
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0950061824002113
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Han, Chengjia
  • Zhang, Jinglin
  • Tu, Zhijia
  • Ma, Tao
• Publication Date: 2024-2-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 135070
• Serial:
  • Construction and Building Materials
  • Volume: 415
  • 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; Mechanical fatigue; Neural networks; Predictive models
• Subject Areas: Highways; Materials; Pavements;

Filing Info

• Accession Number: 01909938
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 27 2024 10:09AM