Over the last three decades, fiber reinforced polymer (FRP) materials have emerged as a promising solution for strengthening and retrofitting of concrete structural members owing to its high strength and durability properties. However, FRP undergoes rapid degradation in strength, modulus, and bond properties due to softening of polymer matrix and bonding adhesive even at moderately elevated temperatures. Therefore, an FRP-strengthened concrete member experiences rapid loss in capacity and stiffness resulting in lower fire resistance than an un-strengthened concrete member. The fire response of FRP-strengthened concrete structural members is influenced by several factors, and thus fire resistance evaluation requires advanced analysis. While several studies are available on fire resistance evaluation of FRP-strengthened reinforced concrete (RC) beams, limited information available on fire performance of FRP-strengthened concrete slabs. Moreover, the available studies on beams do not fully account for all the important factors influencing fire response of strengthened structural members. To address some of the knowledge gaps and to develop a fundamental understanding on the fire resistance of FPR-strengthened RC flexural members, experimental and numerical studies were carried out. As part of experimental studies, a series of tests were conducted at both material level and structural level. For material property characterization, uniaxial tensile tests and double lap shear tests were conducted at elevated temperatures to evaluate high temperature tensile strength of FRP and bond strength of FRP-concrete interface, respectively. For structural fire resistance characterization, tests were conducted on five FRP-strengthened concrete T-beams and two FRP-strengthened concrete slabs, wherein effect of strengthening level, reinforcement ratio, load levels, as well as insulation thickness and configuration was evaluated. As part of numerical studies, a macroscopic finite element based model, originally developed for strengthened RC beams, was further enhanced for evaluating thermo-mechanical response of strengthened RC slabs under fire conditions. The model accounts for temperature dependent material properties, as well as geometric and material nonlinearity. The novelty of model lies in consideration of temperature induced bond degradation through use of different temperature dependent bond-slip relations and in conducting a member level structural analysis rather than analyzing a single critical section. The model was validated using the above generated test data by comparing various response parameters and was applied to quantify the effect of critical factors influencing the fire resistance of FRP-strengthened concrete beams and slabs, through a set of parametric studies. Results from these studies indicate that the fire resistance of FRP-strengthened RC flexural members is significantly influenced by insulation geometry, fire scenario, and load levels, and is moderately influenced by strengthening level or reinforcement ratio. The generated test data as well as those reported in literature were utilized to develop machine learning (ML) based approach for predicting fire resistance of FRP-strengthened concrete beams. Three different ML algorithms, namely support vector regression, random forest regression, and deep neural networks, were successfully trained over the compiled dataset to develop fire resistance prediction models for strengthened RC beams. The accuracy of the trained models was determined by comparing the predictions from the model for an un-seen dataset Results indicate that ML based approaches can be effectively utilized for developing simplified tools for predicting fire resistance of strengthened concrete beams with different geometrical configuration, load levels, reinforcement ratio, and strengthening level.
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