Multivariate structural seismic fragility analysis and comparative study based on moment estimation surrogate model and Gaussian copula function

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.

Automated summary

This paper proposes a parameterized method for component and system-level fragility analysis through multivariate seismic fragility analysis. The study demonstrates that compared to existing methods, the proposed approach can efficiently and accurately generate multivariate fragility functions, and provides optimal choices for sample size, surrogate model, and IM selection.