Machine learning-based prediction of tensile strength of glass fiber-reinforced polymer rebar under environmental conditions
Author(s)
Cheng, Yuqing
Geng, Xiangdong
Wu, Chao
Type
Journal Article
Abstract
GFRP rebar is a desirable alternative to steel rebar especially in harsh environments with advantages of light weight, high strength, stable chemical properties, and corrosion resistance. However, there is a lack of understanding on the durability of GFRP rebar which significantly limits its engineering applications. Unfortunately, it is almost impossible to develop any simple theoretical model to predict the residual strength of GFRP rebar after environmental exposure. To effectively address this research gap, this paper investigates machine learning models to predict the residual tensile strength of GFRP rebar due to environmental degradation. Firstly, a database was built from the literature containing a total of 350 tensile testing results of GFRP rebar after experimental exposure. Six key influencing parameters were considered, including fiber content, bar diameter, resin type, exposure temperature, pH, and aging time. Secondly, machine learning models were trained and tested using the database, and the selected models included Decision Tree, Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Long and Short-Term Memory (LSTM) models. The LSTM model demonstrated the best performance in strength prediction, achieving an R2 of 0.96 on the training set and 0.91 on the testing set. Thirdly, the influencing parameters were ranked using SHAP and Random Forest in terms of their impact on the residual tensile strength of GFRP rebar, and it was found that temperature has the most significant effect followed by fiber content, exposure time, pH and bar diameter, while resin type showed the least importance. Notably, SHAP analysis also showed that the coupling of different parameters also had impact on the residual strength, and the combined effect of fiber content with other parameters was the most prominent. This paper demonstrates the feasibility of machine learning models in the durability study of GFRP composite materials.
Date Issued
2026-04-01
Date Acceptance
2025-07-15
Citation
Advances in Structural Engineering, 2026, 29 (5), pp.831-848
ISSN
1369-4332
Publisher
SAGE Publications
Start Page
831
End Page
848
Journal / Book Title
Advances in Structural Engineering
Volume
29
Issue
5
Copyright Statement
© The Author(s) 2025 This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). Data availability statement
License URL
Identifier
10.1177/13694332251363357
Subjects
ALKALINE
CONCRETE BEAMS
Construction & Building Technology
DEGRADATION
durability
Engineering
Engineering, Civil
GFRP BARS
GFRP rebar
LONG-TERM DURABILITY
machine learning
PERFORMANCE
RODS
Science & Technology
statistical analysis
Technology
tensile strength
Publication Status
Published
Article Number
13694332251363357
Date Publish Online
2025-07-29