Characterizing the Fatigue Life of Asphalt Concrete


The fatigue behavior of asphalt concrete is very complicated that a comprehensive fundamental theoretical model is not available. Therefore, a reliable empirical method for predicting fatigue life based on experimental data remains a desirable approach. However, the complexity of the fatigue process and the noise associated with the fatigue test results make even the traditional empirical methods, such as regression analysis, handicapped in producing a sufficiently accurate model. Artificial neural networks (ANNs) have the ability to derive considerable complex relationships and associations from experimental data while filtering out the effect of noisy data. In this study, the potential use of ANNs for fatigue life prediction was explored and the comparisons between ANN-based model predictions and predictions via multi-linear as well as other published models showed that ANN-based models provide much more accurate predictions.

  • Abstract
  • Introduction
  • ANN Model Development
  • ANN-Based Excel Fatigue Life Prediction Code
  • Comparison of ANN-Based Models with Published Models
  • Concluding Remarks
  • References

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