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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Materials Research (AIMR)
College of Engineering & Physical Sciences > Aston Logistics and Systems Institute
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Road Materials and Pavement Design on 17/3/17, available online:
Uncontrolled Keywords: artificial neural network,finite element model,geogrid-reinforced flexible pavement,pavement ME design,Civil and Structural Engineering
Publication ISSN: 1468-0629
Last Modified: 30 May 2024 07:13
Date Deposited: 19 Aug 2019 10:10
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2018-07-04
Published Online Date: 2017-03-17
Accepted Date: 2017-02-24
Submitted Date: 2016-08-05
Authors: Gu, Fan
Luo, Xue
Zhang, Yuqing (ORCID Profile 0000-0001-5825-0131)
Chen, Yu
Luo, Rong
Lytton, Robert L.



Version: Accepted Version

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