Probabilistic Models for Temperature-Dependent Strength of Steel and Concrete

Structural risk assessment against fire requires robust material models that take into account the uncertainty in material behavior over a range of elevated temperatures. Such probabilistic material models can directly inform performance-based design procedures for building fire safety. The objective of this research is to quantify uncertainties in retained strengths of steel and concrete when exposed to fire. First, hundreds of experimental data points covering a temperature range of 20°C-1,000°C are collected from literature. Then, different distribution candidates and modeling approaches are used with the collected data to identify probabilistic models for temperature dependents strength of steel and concrete. The proposed models are continuous probability distribution functions, with simple mathematical representations that are easy enough to arrange into systematic code for implementation in analytical and computational frameworks. Additionally, the proposed stochastic functions consider continuity in reliability appraisals during transition from room temperature to elevated temperatures. These models are applied to probabilistic evaluations of structural performance of three steel and two concrete columns, and the influence of the model choice is compared using fragility curves. Finally, the proposed probabilistic models, developed using different approaches, led to close results when characterizing the performance of structural members.