Abstract
One of the frontier issues in seismic performance assessment of structures is establishing an accurate and parametric representation between the fragilities and various uncertainty variables. This paper proposes a parameterized component- and system-level fragility analysis method through a multivariate seismic fragility analysis (MSFA) process. To do so, the authors utilize a combination of experimental design schemes with space-filling characteristics, moment estimation based on the surrogate models, and Gaussian copula theory. A case study shows that the proposed method can generate multivariate fragility functions efficiently and accurately compared with the direct Monte Carlo simulation and the existing logistic regression-based method. In addition, to investigate the performance of the proposed method, a comparative study of several critical factors – the sample size of the design matrix, the selection of surrogate models, and IM – is conducted. The necessity of MSFA is confirmed, and the results give the optimal sample size, surrogate model (Gaussian process regression), and IM (SaAVG) for the case.
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