Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

Afifa Tamanna, Ezzatollah Shamsaei, Robert Urquhart, Hoan D. Nguyen, Kwesi Sagoe-Crentsil, Wenhui Duan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This research predicts residual solvent (α), which is a key component of the performance assessment for a sprayed/chip seal. In this study, conventional equations for α were assessed that showed prediction inefficiency (R 2 value as low as 0.82) under different experimental conditions. Accordingly, gene expression programming (GEP), an emerging branch in artificial intelligence, was utilised to resolve these difficulties by developing empirical models for α. The data required for model development was obtained from extensive laboratory tests conducted on bitumen-solvent binder films in this research. Model evaluation results showed an excellent degree of correspondence between predictions and experimental results (R 2 = 0.94). This is the first study to model a key component of sprayed seal performance using GEP. The model is recommended for pre-design purposes or as a tool to determine residual solvent in a sprayed seal when laboratory testing is not feasible, thereby saving time and expenditure.

Original languageEnglish
Pages (from-to)1977-1994
Number of pages18
JournalRoad Materials and Pavement Design
Volume24
Issue number8
DOIs
Publication statusPublished - 2023

Keywords

  • chip seal
  • gene expression programming
  • performance evaluation
  • performance prediction
  • residual solvent
  • Sprayed seal

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