Artificial Neural Network Models for Airport Rigid Pavement Top-Down Critical Stress Predictions: Sensitivity Evaluation

To consider top-down cracking failure in current airport rigid pavement design practices, a multiple-slab pavement structure’s three-dimensional finite element (3D FE) model should be analyzed for determining critical responses associated with such failures, and artificial neural networks (ANNs) can be considered a robust and computationally efficient alternative for 3D FE analysis. This study compares the effects of airport rigid pavement’s most important properties on the critical tensile stresses predicted by the ANN models relative to those for 3D-FE solutions. Sensitivity evaluation of both the 3D-FE solutions and the ANN model predictions for two new large and heavy aircraft (B777-300 ER and B787-8) have been conducted and are discussed in this study. The normalized sensitivity index (NSI) has been utilized to quantify the levels of sensitivity of the critical tensile stresses to changes in PCC slab thickness, base layer thickness, PCC slab modulus, subgrade elastic modulus, temperature gradients, and thermal coefficient. The results demonstrate that the developed ANN model is able to determine top-down critical tensile stress sensitivity similarly to the 3D-FE model.


  • English

Media Info

  • Media Type: Web
  • Pagination: pp 302-312
  • Monograph Title: Airfield and Highway Pavements 2019: Innovation and Sustainability in Highway and Airfield Pavement Technology

Subject/Index Terms

Filing Info

  • Accession Number: 01728625
  • Record Type: Publication
  • ISBN: 9780784482476
  • Files: TRIS, ASCE
  • Created Date: Jul 18 2019 3:04PM