Backcalculation of Layer Parameters of Composite Pavement Systems Using Artificial Neural Networks


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.

  • Abstract
  • Introduction
  • Diplomat Model
  • Neural Networks
  • Neural Network Training and Testing
  • Validation
  • Summary and Conclusions
  • References

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