Condition Assessment of Composite Pavement Systems Using Neural-Network-Based Rapid Backcalculation Algorithms

The objective of this study was to develop artificial neural network (ANN)-based advanced backcalculation models as pavement structural analysis tools for the rapid and accurate prediction of asphalt concrete (AC) overlaid Portland cement concrete (PCC) composite pavement layer moduli under typical highway loadings. The DIPLOMAT program was used for solving deflection profiles of composite pavement systems. The DIPLOMAT solutions were compared with the solutions of ISLAB2000 and ILLI-PAVE pavement analysis programs. ANN-based backcalculation models trained with the results from the DIPLOMAT solutions have been found to be practical alternatives for routine pavement evaluation using the falling weight deflectometer (FWD) deflection data. The trained ANN models in this study were capable of predicting AC and PCC layer moduli, and the coefficient of subgrade reaction value with low average absolute errors. A dimensional analysis approach was also adopted by introducing the dimensional terms of AC modulus over PCC modulus ratio and PCC modulus over coefficient of subgrade reaction ratio value. Both methods were verified by synthetically generated DIPLOMAT deflection profiles. ANN-based backcalculation models developed in this study were also capable of successfully and rapidly (capable of analyzing 100,000 FWD deflection profiles in one second) predicting the pavement layer moduli from the FWD deflection basins in real time during field testing. The developed models were successfully validated by results from the Long-Term Pavement Performance (LTPP) FWD tests conducted on US29, Spartanburg County, South Carolina.


  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; References; Tables;
  • Pagination: 15p
  • Monograph Title: TRB 86th Annual Meeting Compendium of Papers CD-ROM

Subject/Index Terms

Filing Info

  • Accession Number: 01043596
  • Record Type: Publication
  • Report/Paper Numbers: 07-3356
  • Files: PRP, TRIS, TRB
  • Created Date: Feb 8 2007 8:02PM