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Compressive Strength Prediction of Green Concrete with Recycled Glass-Fiber-Reinforced Polymers Using a Machine
Pouyan Fakharian1,2, Reza Bazrgary3, Ali Ghorbani4
1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
Polymers
|October 28, 2025
Summary
This study used machine learning to predict the compressive strength of concrete with recycled glass-fiber-reinforced polymer (GFRP) waste. Results show that lower GFRP content and specific forms like powders or fibers enhance concrete strength.
Area of Science:
- Materials Science
- Civil Engineering
- Sustainable Construction
Background:
- Growing waste from fiber-reinforced polymer (FRP) in construction and transportation industries.
- Need for sustainable concrete alternatives and waste valorization.
Purpose of the Study:
- To predict the compressive strength of eco-friendly concrete with recycled glass-fiber-reinforced polymer (GFRP) waste using machine learning.
- To identify key factors influencing concrete strength with GFRP incorporation.
Main Methods:
- Development and application of a machine learning model.
- Laboratory testing of 119 concrete mixes with varying GFRP waste content and forms.
- Statistical analysis of influential parameters.
Main Results:
- Machine learning model achieved high prediction accuracy (R² = 0.8284).
- Compressive strength generally decreases with higher GFRP content, but is favorable at low dosages.
- Water-to-cement ratio and total GFRP content were most influential.
- Powder and fiber forms of GFRP showed positive effects; aggregate replacement was less effective.
Conclusions:
- Machine learning provides quantitative guidance for designing sustainable concrete with GFRP waste.
- Highlights potential for reusing industrial by-products in construction materials.
- Optimizing GFRP form and content is crucial for performance.
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