Concrete under fire: an assessment through intelligent pattern recognition

Concrete, a naturally resilient material, often undergoes a series of physio-chemical degradations once exposed to extreme environments (e.g., elevated temperatures). Under such conditions, not only concrete weakens, but also becomes vulnerable to fire-induced spalling; a complex and exceptionally random phenomenon. Despite serious efforts carried out over the past few years, we continue to be short of developing a methodical procedure that enables accurate assessment of concrete under elevated temperatures with due consideration to fire-induced spalling. Unlike traditional works, this study aims at investigating fire behavior of concrete through a modern perspective. In this study, a number of intelligent pattern recognition (IPR) techniques that capitalize on artificial intelligence (AI) are applied to derive expressions able of accurately trace the response of normal and high strength as well as high performance concretes under elevated temperatures. These expressions take into account geometric, material, and specific features of structural components to examine fire response as well as to predict occurrence of fire-induced spalling in concrete structures. These expressions were developed through rigorous and data-driven analysis of actual fire tests and were derived to implicitly account for physio-chemical transformations in concrete and as such do not require collection/input of temperature-dependent material properties nor special analysis/simulation. This study also features the development of an IPR-based database and fire assessment software that can be used to examine fire performance of concrete members and be regularly updated as to continually improve the accuracy of the proposed expressions.