Applications of Artificial Neural Networks to Pavement Prediction Modeling: A Case Study

Artificial neural networks (ANN) have been used in many pavement prediction modeling analyses. However, the convergence characteristics and model selection guidelines are rarely studied due to the requirement of extensive network training time. Thus, the techniques and applications of back propagation neural networks were briefly reviewed. Three ANN models were developed using deflection databases generated by factorial bistatic synthetic aperture radar (BISAR) runs. A study of the convergence characteristics indicated that the resulting ANN model using all dominating dimensionless parameters was proved to have higher accuracy and require less network training time and data than the other counterpart using purely input parameters. Increasing the complexity of ANN models does not necessarily improve the modeling statistics. With the incorporation of subject-related engineering and statistical knowledge into the modeling process, reasonably good predictions may be achieved with more convincing generalization and explanation yet requiring minimal amount of time and effort.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 289-295
  • Monograph Title: Challenges and Advances in Sustainable Transportation Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01527148
  • Record Type: Publication
  • ISBN: 9780784413364
  • Files: TRIS, ASCE
  • Created Date: May 15 2014 3:02PM