Maximizing information obtained from standardized fire tests using a Bayesian framework

Current practice is mostly focused on prescriptive design approaches where the performance of the structure in case of fire is assessed based on its performance in standardized fire tests. Those tests indicate whether the structural member can withstand standardized ISO 834 fire exposure for a certain code specified time. This method however does not provide an explicit safety level. This issue is enlarged even more by the fact that the ISO 834 fire exposure does not represent a natural fire exposure, but a pseudo-worst-case exposure, making the correlation between the standardized fire test results and real-life behaviour of structural members exposed to fire questionable. However, there has been a century-old tradition of standardized fire tests with a lot of experience and infrastructure based on it. For that reason, here, a methodology is presented to obtain more information on the behaviour of structural members exposed to a natural fire from the standardized fire test results by using a Bayesian framework. As an example structure, a simply supported concrete slab is considered. Its failure during the standardized fire test is modelled and the parameters affecting the time when it fails (i.e., parameters affecting the nominal fire resistance time) are determined. The model is then used in a Markov chain Monte Carlo procedure to update parameter distributions based on the measured fire resistance time. Using these updated distributions, a full probabilistic calculation of the performance of the slab considering a natural fire exposure is then conducted to assess the failure probability.