Probabilistic Seismic Demand Analysis Considering Random System Properties by an Improved Cloud Method

Abstract eng:
Probabilistic seismic demand analysis (PSDA) is an approach for computing the mean annual frequency of exceeding a specified seismic demand for a given structure at a designated site, which combines the conventional probabilistic seismic hazard analysis (PSHA) of the designated site with nonlinear dynamic analysis (NDA) of the given structure. Now PSDA has already become an important part of the new-generation Performance Based Earthquake Engineering (PBEE) proposed by PEER. The commonly used nonlinear demand estimation method in PSDA is the so called “cloud analysis” procedure in which a cloud of demand values are generated when a structure is subjected to a suite of earthquake ground motion records. However, this method can only take into account the record-to-record (RTR) variation in ground motions without considering the random system properties of structures. In this paper, an improved cloud method based on Latin Hypercube Sampling (LHS) method is proposed to build probabilistic seismic demand models (PSDM) to consider inherent randomness both in structures and in ground motions. Furthermore, probabilistic seismic demand fragility analysis (PSDFA) and probabilistic seismic demand hazard analysis (PSDHA) are made based on the improved probabilistic seismic demand models. The methodology is applied in a three-bay and five-storey R.C. frame as an example. The probabilistic seismic demand models with the conventional cloud method and the improved cloud method are compared, and the probabilistic seismic demand fragility curves and hazard curves are derived respectively.

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