A Mechanistic–Empirical Model for Top–Down Cracking of Asphalt Pavements Layers

The objective of this research project is to develop a calibrated mechanistic-empirical (ME) model for predicting the load-related top-down cracking in the asphalt layer of flexible pavements, and associated computational software for incorporation into the AASHTOWare Pavement ME Design software. The project required the development and synthesis of eight components: (a) laboratory testing of asphalt field cores for complex modulus gradient and master curve; (b) kinetics-based modeling of long-term field aging in asphalt pavements; (c) finite element computations of the J-integral at the crack tip; (d) use of the finite element program to develop full factorial sets of pavement data to construct Artificial Neural Network (ANN) models for the J-integral at the crack tip; (e) prediction of top-down cracking due to thermal loading based on the J-integral under thermal stresses; (f) develop a top-down crack initiation model and a crack growth model under traffic loading and environmental effect; (g) generation of a cumulative damage model to predict top-down cracking propagation; and (h) development and calibration of top-down cracking prediction model. Major deliverables of this project also include computer subroutines written for incorporation into the Pavement ME Design software to predict top-down cracking in asphalt pavement layers. The programs are supported by test and analysis methods that determine the material properties, fracture properties, and aging properties for top-down cracking in asphalt pavement layers. These are the inputs that the Pavement ME Design software needs in order to accurately predict the appearance and growth of top-down cracking. The catalog of measured and collected data in electronic form is another major deliverable of this project. All of these properties are inputs for the finite element analysis of the J-integral for top-down cracking. Multiple runs with the finite element program over a wide range of pavement variables produce the data for constructing Artificial Neural Network (ANN) models of the J-integral at the tip of the crack, which drives the crack to propagate downward from the surface.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Contractor's Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 218p
  • Serial:
  • Publication flags:

    Open Access (libre)

Subject/Index Terms

Filing Info

  • Accession Number: 01685011
  • Record Type: Publication
  • ISBN: 9780309486217
  • Report/Paper Numbers: NCHRP Project 01-52
  • Files: TRIS, TRB, ATRI
  • Created Date: Nov 5 2018 11:43AM