Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to conditions, masonry undergoes a series physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization such models, there is lack in standardized As result, available models vary across various codes standards. In order bridge this knowledge gap, paper presents three methodologies, namely, regression-based, probabilistic-based, the use artificial neural (ANN) networks, derive generalized with case study on compressive strength property. Findings can be adopted establish updated design analysis structures.