Improved probability distribution models for seismic fragility assessment Qin, Jianjun; Mackie, Kevin R.; Stojadinovic, Bozidar
The paper presents a new formulation for the probabilistic modeling of the variables to reduce the errors between the model and the available data sets and to facilitate seismic fragility assessment. Traditionally, one probability distribution, e.g., lognormal distribution, is adopted to describe one variable throughout the domain of values. However, there are potential errors in the probability content, especially in the high tail. The consequences of such tail sensitivity are particularly detrimental in fragility assessment when the limit states under consideration result in small probabilities of failure. To address the tail sensitivity problem, the data sets of the variables are divided into two parts, i.e., bulk and high tail. Each part is considered separately for the probabilistic modeling and integrated into one continuous distribution for fragility analysis. For illustration purposes, probabilistic seismic assessment of a typical reinforced concrete bridge column is compared using the proposed framework and compared to the results based only on lognormal random variable distributions. Results show that for complementary cumulative distribution function (CCDF), the difference may reach one or more magnitudes; for limit states that exercise the tails of the random variable distributions, the difference in the fragility estimates can increase with the hazard level.
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