Analysis of RC T-beams strengthened with CFRP plates under fire loading using ANN

This paper presents an alternative approach to predicting fire resistance of reinforced concrete (RC) T-beams that are both strengthened with Carbon Fiber Reinforced Polymers (CFRPs) plates and insulated with different protecting materials, and exposed to different fire scenarios. Based on both experimental and Finite Element (FE) studies, Artificial Neural Networks (ANNs) were used to extend earlier research. The developed ANN can be used to predict the fire endurance of RC T-beams strengthened with CFRP laminates when subjected to elevated temperatures. Different insulation thicknesses, materials types and fire curves were the input parameters in the presented ANN. The predicted fire endurance and time to failure are compared with obtained experimental and validated FE simulation results. Strong correlation between the predicted ANN, experimental, and FE results was obtained. The developed ANN model was then expanded in an extensive parametric study to predict the fire resistance of the strengthened beam using different insulation thicknesses, insulation material types, and fire scenarios. Design charts were also developed to be used as preliminary guidelines to aid designers in selecting the required insulation thickness for specific insulation systems and fire exposure scenarios. It was concluded that the developed and validated ANN could be used as a computational tool in the analysis and design of RC beams strengthened with CFRP plates and subjected to thermal fire loadings. Other conclusions and observations were also drawn.