Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives

Concrete structures when exposed to elevated temperature significantly decline their original properties. High temperatures substantially affect the concrete physical and chemical properties causing significant structural decay and generalized damage impairing the safety and serviceability of the structure. Due to the great importance of concrete behavior at elevated temperatures and under fire, many studies have been conducted on cementitious composites, and the most relevant properties have been studied and evaluated. In particular, fiber-reinforced concrete (FRC) has been a subject of great interest in the last decade due to its superior properties compared to ordinary concrete. Several experimental studies and analytical models have been presented to predict concrete and FRC properties. Among the predictive models, machine learning (ML) tools have shown great merits over other analytical models due to their relative accuracy, generalization abilities, flexible mathematical framework, and cost-effective features. Among the ML, the deep learning (DL) models show remarkable performance when predicting the concrete and the FRC properties at high temperatures because of their ability to deal with more complex nonlinear correlations or difficult regression problems. This review paper presents a pioneering survey of the various ML and DL model implementations predicting concrete and FRC properties at high temperatures. The manuscript aims to establish a solid platform on the state of the art for machine and deep learning prediction of cementitious composites' properties at elevated temperatures. It aims to provide interested researchers with research indications, directions, challenges, recommendations, and future perspectives.