Scholarlink

<p>The structural integrity of reinforced concrete (RC) members retrofitted with externally bonded composite laminates is primarily governed by complex interfacial stress transfer mechanisms, which are frequently compromised by manufacturing imperfections and aggressive environmental exposure. While conventional fiber-reinforced polymer strengthening is well established, the transition toward bio-inspired helicoidal Bouligand architectures offers a transformative approach to enhancing delamination resistance. However, the interfacial mechanics of these advanced configurations under coupled multi-physical loading remain insufficiently characterized. This study presents a robust computational framework leveraging high-precision artificial neural networks to evaluate interfacial shear and normal stress distributions in RC beams featuring stochastic air-void defects. Utilizing an extensive database of 63,975 data points, a multi-layer perceptron architecture was optimized to capture the high-dimensional nonlinear coupling between constitutive material properties, geometric configurations, and tri-stage loading. The model demonstrates exceptional predictive fidelity, yielding an overall correlation coefficient Rall = 0.99915. Principal results reveal that the bio-inspired helicoidal technique significantly homogenizes interfacial stress distribution, effectively mitigating peak concentrations at the plate ends. Parametric investigations demonstrate that optimizing the reinforcement density (12–32 layers) and fiber architecture facilitates a global reduction in peak interfacial shear and normal stresses by up to 40%. Notably, hygrothermal concentrations escalate debonding driving forces by up to 30%, while the helicoidal configuration attenuates the adverse effects of stochastic air-void defects. This research establishes the bio-inspired technique as a superior, computationally efficient alternative for the reliability-based design and optimization of resilient infrastructure.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of interfacial stresses in helicoidal FRP-strengthened RC beams under hygrothermal effects

  • 期刊论文

  • 发表日期：

    2026年4月30日

查看全文
  • Mokhtar Nebab,
  • Rezki Amara,
  • Hassen Ait Atmane,
  • Fabrice Bernard,
  • Riadh Bennai

摘要

The structural integrity of reinforced concrete (RC) members retrofitted with externally bonded composite laminates is primarily governed by complex interfacial stress transfer mechanisms, which are frequently compromised by manufacturing imperfections and aggressive environmental exposure. While conventional fiber-reinforced polymer strengthening is well established, the transition toward bio-inspired helicoidal Bouligand architectures offers a transformative approach to enhancing delamination resistance. However, the interfacial mechanics of these advanced configurations under coupled multi-physical loading remain insufficiently characterized. This study presents a robust computational framework leveraging high-precision artificial neural networks to evaluate interfacial shear and normal stress distributions in RC beams featuring stochastic air-void defects. Utilizing an extensive database of 63,975 data points, a multi-layer perceptron architecture was optimized to capture the high-dimensional nonlinear coupling between constitutive material properties, geometric configurations, and tri-stage loading. The model demonstrates exceptional predictive fidelity, yielding an overall correlation coefficient Rall = 0.99915. Principal results reveal that the bio-inspired helicoidal technique significantly homogenizes interfacial stress distribution, effectively mitigating peak concentrations at the plate ends. Parametric investigations demonstrate that optimizing the reinforcement density (12–32 layers) and fiber architecture facilitates a global reduction in peak interfacial shear and normal stresses by up to 40%. Notably, hygrothermal concentrations escalate debonding driving forces by up to 30%, while the helicoidal configuration attenuates the adverse effects of stochastic air-void defects. This research establishes the bio-inspired technique as a superior, computationally efficient alternative for the reliability-based design and optimization of resilient infrastructure.