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June 4, 20260 citationsOpen Access

Optimizing Bond Strength and Slip Mechanisms between GFRP Composite Rebars and Concrete Using Artificial Intelligence

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AAAli Alemi

Key Points

  • The aim is to enhance the understanding and optimization of bond behavior between GFRP rebars and concrete.
  • Used artificial intelligence to analyze bond strength and slip mechanisms
  • Considered various factors such as fiber orientation and surface modification
  • Evaluated performance through simulations based on existing empirical data
  • Improved predictions of bond strengths with AI modeling
  • Identified optimal surface modifications for enhanced bond behavior
  • Demonstrated that traditional models inadequately capture bond-slip relationships

Abstract

Glass Fiber Reinforced Polymer (GFRP) composite rebars are increasingly adopted in reinforced concrete (RC) structures, particularly in aggressive or high-durability environments, due to their superior corrosion resistance and light weight compared to conventional steel reinforcement. However, the inherent differences in material behavior, surface interaction, and manufacturing processes between GFRP and steel lead to complex and often less understood bond behavior. The bond strength and the resulting slip mechanisms between GFRP and concrete dictate the serviceability and ultimate limit states of the structure, governing crack control, load transfer efficiency, and structural robustness. Traditional analytical models and design codes often simplify this complex tri-linear bond-slip relationship (initial stiffness, peak bond strength, and post-peak softening) based on empirical fitting to limited experimental data, failing to capture the nuances introduced by fiber orientation, surface modification (ribs/sand coating), matrix composition, and environmental aging.

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Cite This Study

Ali Alemi (2025) studied this question.

synapsesocial.com/papers/6a2117bfd499ed480b17090chttps://doi.org/10.82485/bim.2026.1229972
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