Efficient surrogate method for predicting pavement response to various tire configurations

Mojtaba Ziyadi, Imad L. Al-Qadi

Research output: Contribution to journalArticlepeer-review


A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite element models representing low-volume (thin) and high-volume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels—detailed and simplified—depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5 % and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base versus dual tire assembly under typical loading conditions and pavement structures.

Original languageEnglish (US)
Pages (from-to)1355-1367
Number of pages13
JournalNeural Computing and Applications
Issue number6
StatePublished - Jun 1 2017


  • Finite element modeling
  • Neural networks
  • Pavement response prediction
  • Surrogate model
  • Thin and full-depth pavement
  • Wide-base tire

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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