Skip to Main Content
ASME Press Select Proceedings

Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
Search for other works by this author on:
Anna L. Buczak
Anna L. Buczak
Search for other works by this author on:
David L. Enke
David L. Enke
Search for other works by this author on:
Mark Embrechts
Mark Embrechts
Search for other works by this author on:
Okan Ersoy
Okan Ersoy
Search for other works by this author on:
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

This paper describes the use of Artificial Neural Networks (ANNs) as pavement structural analysis tools for the rapid and accurate prediction of layer parameters of asphalt overlaid Portland Cement Concrete (PCC) composite pavements subjected to typical highway loadings. The DIPLOMAT program was used for solving the deflection parameters of composite pavements. ANN models trained with the results from the DIPLOMAT solutions have been found to be practical alternatives. The trained ANN models in this study were capable of predicting Asphalt Concrete (AC) and PCC moduli, and coefficient of subgrade reaction (ks) with low Average Absolute Errors (AAEs). ANN backcalculation models were also capable of successfully predicting the pavement layer moduli from the Falling Weight Deflectometer (FWD) deflection basins and they may be used in the field for rapidly assessing the condition of pavement sections during the FWD testing. The developed method was successfully verified using results from Long-Term Pavement Performance (LTPP) FWD tests conducted at US29, Spartanburg County, South Carolina.

This content is only available via PDF.
Close Modal
This Feature Is Available To Subscribers Only

Sign In or Create an Account

Close Modal
Close Modal