01043543

Component

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.

01042056

07-1607

English

19p

2007

Transportation Research Board 86th Annual Meeting

CD-ROM

Figures (4) ; References (12) ; Tables (2)

Asphalt concrete; Asphalt concrete pavements; Cracking; Cracking of asphalt concrete pavements; Empirical methods; Failure; Fatigue (Mechanics); Information management; Neural networks; Regression analysis

Data filtering

Data and Information Technology; Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways

Transportation Research Board Annual Meeting 2007 Paper #07-1607

TRIS, TRB

Feb 8 2007 6:16PM