Influence of Ground-Motion Intensity Measure Selection on Bayesian Fragility Analysis of RCS Frame Structures

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Abstract
This study focuses on RCS frame structures and selects six different types of ground-motion intensity measures (IMs), including peak ground acceleration (PGA), spectral acceleration at the fundamental period Sa(T1), the modified intensity measure S* considering period elongation effects, IM12 and IM123 accounting for higher-mode effects, and Housner intensity (HI). Based on a set of near-fault pulse-like ground-motion records, a Bayesian seismic fragility analysis characterized by different IMs is conducted. This study reveals the influence of these IMs on the estimation of fragility parameters under three limit states—immediate occupancy (IO), life safety (LS), and collapse prevention (CP)—using both uniform non-informative priors and lognormal weakly informative priors. The results indicate that, in terms of the applicability of IMs across different limit states, all IMs exhibit highly stable fragility parameters in the elastic IO stage, where the results from maximum likelihood estimation (MLE), uniform priors, and lognormal priors are nearly identical, suggesting that sufficient sample information renders the influence of priors negligible. In contrast, in the CP stage, characterized by strong nonlinearity and collapse, the differences among IMs become most pronounced. HI consistently yields stable results across all methods with almost no variation. When the structure enters the CP stage with small samples and strong nonlinearity, the lognormal prior effectively promotes distribution convergence, suppresses over-dispersion, and corrects asymmetry, significantly improving the robustness of parameter estimation. Notably, different IMs exhibit varying sensitivity to Bayesian priors, among which S* and HI are the least sensitive, demonstrating strong inherent stability and minimal dependence on prior constraints.
Funding Information
  • National Natural Science Foundation of China (52108457)
  • China Postdoctoral Science Foundation (2023M741731)
  • Natural Science Foundation of Qingdao Municipality (23-2-1-102-zyyd-jch)
  • Natural Science Foundation of Shandong Province (ZR2024QE393)
  • Natural Science Foundation of Jiangsu Province (BK20230400)
  • Jiangsu Undergraduate Innovation Training Program (202510298044Z)

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