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First published online January 1, 2017

Numerical Modeling and Artificial Neural Network for Predicting J-Integral of Top-Down Cracking in Asphalt Pavement

Meng Ling [email protected], Xue Luo, Sheng Hu, Fan Gu, and Robert L. LyttonView all authors and affiliations
Volume 2631, Issue 1
https://doi.org/10.3141/2631-10

Abstract

Top-down cracking (TDC) is recognized as one of the major distress modes in asphalt pavements. This study aimed to determine the fracture parameter J-integral of TDC, which is a critical input to predict the crack growth rate and fatigue life of pavements for this type of distress. Previous research studies demonstrated that TDC is affected by various factors, including the complex state of high tensile or shear stresses induced by the loading at the edge of or within the tire and material properties such as the modulus gradient in the asphalt layer, moduli of the base and subgrade layers, and pavement structures. In this study, the finite element model (FEM) was adopted to simulate the propagation of TDC by considering combinations of these essential factors and to calculate the J-integral for 194,400 cases. It was shown that the modulus gradient plays an important role in determining the J-integral, and the J-integral is not uniformly distributed within the pavement depth. On the basis of the database generated from the FEM, six backpropagation artificial neural network (ANN) models—including one input layer, two hidden layers, and one output layer—were developed by using the same input variables and output variable as those for the FEM. The R2 value for each ANN model was greater than .99, which indicates the goodness of fit. After the parameters of each ANN model have been determined, the J-integral can be predicted for any combination of the design parameters without reconstruction of the FEM.

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Published In

Volume 2631, Issue 1
Pages: 83 - 95
Article first published online: January 1, 2017
Issue published: January 2017

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© 2017 National Academy of Sciences.
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Authors

Affiliations

Meng Ling
601C, Texas A&M Transportation Institute, Dwight Look College of Engineering, 503A, Texas A&M University, 3135 TAMU, CE–TTI Building, College Station, TX 77843
[email protected]
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Xue Luo
508B, Texas A&M Transportation Institute, Dwight Look College of Engineering, 503A, Texas A&M University, 3135 TAMU, CE–TTI Building, College Station, TX 77843
View all articles by this author
Sheng Hu
303G, Texas A&M Transportation Institute, Dwight Look College of Engineering, 503A, Texas A&M University, 3135 TAMU, CE–TTI Building, College Station, TX 77843
View all articles by this author
Fan Gu
National Center for Asphalt Technology, Auburn University, 277 Technology Parkway, Auburn, AL 36830
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Robert L. Lytton
Zachry Department of Civil Engineering, Dwight Look College of Engineering, 503A, Texas A&M University, 3135 TAMU, CE–TTI Building, College Station, TX 77843
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