Bayesian calibration and reliability analysis of ultra high-performance fibre reinforced concrete beams exposed to fire

Abstract

A study of uncertainty quantification and reliability of ultra-high-performance fibre reinforced concrete (UHPFRC) beams exposed to fire is presented. The study sets forth two methodologies, the first involving the stochastic calibration of temperature-dependent thermal properties of UHPFRC via Bayesian inversion, and the second concerning the computation of the reliability of UHPFRC beams in fire via Monte Carlo Simulation (MCS). Using the calibrated posterior probability distributions of temperature-dependent thermal properties of UHPFRC in stochastic heat transfer analyses results in predictive temperature distributions that reasonably track (within 95% confidence interval) the temperature response reported on UHPFRC beams specimen subjected to 80 and 60 minutes standard fire. Stochastic temperature distributions based on the calibrated thermal properties were sequentially transferred via a time series surrogate model, into a structural-fire reliability analysis of UHPFRC beams designed to the specifications of the ACI 544. Reliability analysis is conducted and the obtained reliability index generally reduced with increasing fire exposure for the UHPFRC beam considered and fire resistance is estimated based on a reliability target of 0.7.