Surrogate SDOF models for probabilistic performance assessment of multistory buildings: Methodology and application for steel special moment frames. (1st June 2020) - Vaseghiamiri, Shaghayegh; Mahsuli, Mojtaba; Ghannad, Mohammad Ali; Zareian, Farzin
Highlights: Surrogate SDOF for the estimation of roof drift probability distribution is outlined. The surrogate SDOF model explicitly accounts for model uncertainties. Such surrogate models can be used for regional risk analyses and parametric studies. The surrogate SDOF models are validated for steel special moment frame buildings. Application of the developed surrogate SDOF model is demonstrated using two examples. Abstract: This paper proposes a methodology for generating surrogate single-degree-of-freedom (SDOF) models that can be utilized to estimate the probability distribution of the roof drift ratio of multistory buildings at various ground motion intensity measures. The use of an SDOF model as a surrogate for multistory buildings can significantly alleviate the high computational cost for probabilistic seismic demand assessment considering both model uncertainty and record-to-record variability. The surrogate SDOF model generated herein explicitly accounts for model uncertainties and can be used as an alternative to the nonlinear dynamic analysis of detailed building structures. Applications for such surrogate models include regional risk and resilience analyses and comprehensive parametric studies. To showcase the proposed methodology, an SDOF surrogate model for steel special moment frame (SMF) buildings is developed using the suggested surrogate SDOF model generating methodology. The properties of the surrogate model representing a multi-degree-of-freedom (MDOF) structure are computed using a probabilistic function of the fundamental period of the structure developed using Bayesian linear regression. To validate the surrogate model for SMFs, the response statistics produced using detailed multistory SMF models are compared with those of the corresponding surrogate SDOF models. The results show that the proposed surrogate SDOF model captures the probability distribution of the roof drift ratio of SMFs up to collapse with acceptable accuracy while reducing the runtime by at least one order of magnitude.