An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

Al-Hamd, Rwayda Kh S.; Albostami, Asad S.; Alzabeebee, Saif; Al-Bander, Baidaa

Abstract

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars' bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).

The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete.

Citation

Al-Hamd, R. K. S., Albostami, A. S., Alzabeebee, S., & Al-Bander, B. (2024). An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques. Journal of Building Engineering, 86, Article 108883. https://doi.org/10.1016/j.jobe.2024.108883

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2024
• Online Publication Date: Feb 24, 2024
• Publication Date: Jun 1, 2024
• Deposit Date: Jun 7, 2024
• Journal: Journal of Building Engineering
• Electronic ISSN: 2352-7102
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 86
• Article Number: 108883
• DOI: https://doi.org/10.1016/j.jobe.2024.108883
• Public URL: https://keele-repository.worktribe.com/output/847062
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2352710224004510?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques; Journal Title: Journal of Building Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jobe.2024.108883; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier Ltd.