Experimental and numerical study on FRP-rehabilitated RC beam-column joints at high temperature with artificial...

This research explores the structural integrity and thermal durability of reinforced concrete beam-column joints rehabilitated with fiber-reinforced polymer (FRP) laminates under elevated temperatures reaching 800 °C. The work addresses a significant knowledge deficiency by combining computational, experimental, and machine learning methodologies to thoroughly assess FRP performance under thermal stress, a subject inadequately explored in previous literature. A total of 36 models, including 9 conventional and 27 rehabilitated configurations, were analyzed through coupled thermo-mechanical simulations using finite element software. The novelty lies in the pre-experimental identification of critical regions and optimal materials via numerical analysis. At 500 °C, CFRP-rehabilitated joints reduced deflection by up to 42.8% and stress by 37.2% compared to GFRP. In contrast, AFRP showed over 60% higher deflection. The most vulnerable area was identified as the joint core, especially on the column face adjacent to the beam. Experimental tests confirmed CFRP's superiority; with specimens showing a 28.5% higher load capacity and 31.6% lower core temperature at failure than GFRP-enhanced specimens. Artificial neural network (ANN) regression models were developed to predict deflection and nodal temperature based on input parameters. These models yielded high accuracy (R² > 0.99), closely matching experimental and numerical results. However, generalizing predictions beyond the studied range may introduce over fitting risks, and the model remains sensitive to data quality. In summary, CFRP demonstrated optimal performance, particularly at 400 °C before rehabilitation and 500 °C afterward, making it the most effective choice for high-temperature FRP-based RC joint rehabilitation. This integrated methodology presents an all comprehensive structure for performance-oriented FRP restoration of reinforced concrete joints.