Hybrid Bayesian-Copula-based damage probability estimation for steel-concrete composite tall buildings under concurrent seismic and wind loads

Abstract

Tall buildings with long service periods inevitably face multiple hazards, and the uncertainty associated with various factors has a considerable impact on life-cycle structural safety estimation. This study presents a hybrid Bayesian-Copula-based methodology for evaluating the damage risk e of tall buildings under concurrent seismic and strong wind excitations that incorporate various uncertainties. The main contributions of this study to the field of probabilistic multi-hazard risk assessment include the following: (1) The Bayes statistic method is employed to develop posterior probability distributions of the unknown parameters in the marginal probability models of an individual earthquake and strong wind as well as parameters involved in the multi-hazard demand model for fragility estimation. (2) The Bayesian-based method is applied to update the existing joint probabilistic model of earthquakes and strong winds. (3) A new method is presented to estimate the muti-hazard fragility bounds. The damage risk assessment quantifies the epistemic uncertainties of the unknown demand model parameters by calculating the total probability in the domain of the definition of the model parameters. A representative composite building with 42 floors is selected to perform this multi-hazard damage risk assessment method. The application of this study highlights the considerable impact of epistemic uncertainties and loading directions on damage risk. This presented Bayesian-Copula-based method is beneficial for decision-making involving tall buildings subjected to multiple hazards.