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Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance1.88 MB
Journal article
Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance
Road materials and pavement design, Vol.24(8), pp.1977-1994
03/08/2023
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Source: InCites
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 (R2 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 (R2 = 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.
Details
- Title
- Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance
- Creators
- Afifa Tamanna - Monash UniversityEzzatollah Shamsaei - Monash UniversityRobert Urquhart - Australian Road Research Board (ARRB), Port Melbourne, AustraliaHoan Nguyen - Monash UniversityKwesi Sagoe-Crentsil - Monash UniversityWenhui Duan - Monash University
- Publication Details
- Road materials and pavement design, Vol.24(8), pp.1977-1994
- Publisher
- Taylor & Francis
- Identifiers
- 991013171713802368
- Copyright
- © 2022 Informa UK Limited, trading as Taylor & Francis Group.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article