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This study employs tree-based machine learning models to predict the tensile strength retention (TSR) of Fibre Reinforced Polymer (FRP) composites subjected to elevated temperatures, utilizing a database of 787 experimental results. Key factors influencing TSR include exposure temperature, resin glass transition temperature, and sample thickness, with exposure temperature being the most significant. The research aims to enhance predictive modeling for the safe application of FRP composites in various engine

Author

24022962

Language

EN