Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach

This study aimed to develop a methodology to incorporate geogrid material into the Pavement ME Design software for predicting the geogrid-reinforced flexible pavement performance. A large database of pavement responses and corresponding material and structure properties were generated based on numerous runs of the developed geogrid-reinforced and unreinforced pavement models. The artificial neural network (ANN) models were developed from the generated database to predict the geogrid-reinforced pavement responses. The developed ANN models were sensitive to the change of base and subgrade moduli, and the variation of geogrid sheet stiffness and geogrid location. The ANN model-predicted geogrid-reinforced pavement responses were then used to determine the modified material properties due to geogrid reinforcement. The modified material properties were finally input into the Pavement ME Design software to predict geogrid-reinforced pavement performance. The ANN approach was rapid and efficient to predict geogrid-reinforced pavement performance, which was compatible with the Pavement ME Design software.

• Record URL:
  https://doi.org/10.1080/14680629.2017.1302357
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14680629
• Supplemental Notes:
  • © 2017 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Gu, Fan
  • Luo, Xue
  • Zhang, Yuqing
  • Chen, Yu
  • 0000-0001-9459-9349
  • Luo, Rong
  • Lytton, Robert L
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1147-1163
• Serial:
  • Road Materials and Pavement Design
  • Volume: 19
  • Issue Number: 5
  • Publisher: Taylor & Francis
  • ISSN: 1468-0629
  • EISSN: 2164-7402
  • Serial URL: http://www.tandfonline.com/loi/trmp20

Subject/Index Terms

• TRT Terms: Finite element method; Flexible pavements; Geogrids; Mechanistic-empirical pavement design; Neural networks; Pavement performance
• Subject Areas: Highways; Materials; Pavements;

Filing Info

• Accession Number: 01671177
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2018 9:36AM