Analytical fragility assessment using unscaled ground motion records

TLDR

Structural fragility assessment ideally uses unscaled ground motions, but the Cloud Analysis method—relying on such records and linear regression—has been limited by perceived inaccuracies, and applying its record‑selection rules often necessitates handling collapse cases. The study investigates Cloud Analysis–based fragility assessment using a demand‑to‑capacity ratio equal to one at the limit state. The authors select records ensuring a substantial portion exceed the limit state and exhibit wide intensity dispersion, then model collapse cases with a 5‑parameter logistic‑regression hybrid, estimating fragility parameters via MCMC to obtain curves and confidence intervals. The Cloud Analysis, when applied with carefully selected records, yields reasonable and efficient fragility estimates that compare favorably.

Abstract

Summary It is desirable that nonlinear dynamic analyses for structural fragility assessment are performed using unscaled ground motions. The widespread use of a simple dynamic analysis procedure known as Cloud Analysis, which uses unscaled records and linear regression, has been impeded by its alleged inaccuracies. This paper investigates fragility assessment based on Cloud Analysis by adopting, as the performance variable, a scalar demand to capacity ratio that is equal to unity at the onset of limit state. It is shown that the Cloud Analysis, performed based on a careful choice of records, leads to reasonable and efficient fragility estimates. There are 2 main rules to keep in mind for record selection: to make sure that a good portion of the records leads to a demand to capacity ratio greater than unity and that the dispersion in records' seismic intensity is considerable. An inevitable consequence of implementing these rules is that one often needs to deal with the so‐called collapse cases. To formally consider the collapse cases, a 5‐parameter fragility model is proposed that mixes the simple regression in the logarithmic scale with logistic regression. The joint distribution of fragility parameters can be obtained by adopting a Markov Chain Monte Carlo simulation scheme leading directly to the fragility and its confidence intervals. The resulting fragility curves compare reasonably with those obtained from the Incremental Dynamic Analysis and Multiple Stripe Analysis with (variable) conditional spectrum–compatible suites of records at different intensity levels for 3 older reinforced concrete frames with shear‐, shear‐flexure‐, and flexure‐dominant behavior.