Dataset on fire resistance analysis of FRP-strengthened concrete beams

Machine learning (ML) has emerged as an efficient and feasible technique for tackling engineering problems. Despite the numerous advantages, the implementation of ML for evaluating the fire resistance of structural members is relatively scarce, primarily due to the lack of a reliable database with a substantial number of data points. To address this knowledge gap, this paper presents a comprehensive database on the fire performance of fiber reinforced polymer (FRP) strengthened reinforced concrete (RC) beams. The database comprises over 21,000 experimental and numerical data points with varying parameters, including various geometric dimensions, FRP-strengthening levels, steel reinforcement ratio, insulation thickness and configuration, material properties, and applied load levels. The database can be implemented to train ML algorithms for developing autonomous models for predicting the fire resistance of FRP-strengthened concrete beams with varying parameters.