Assessment of the effectiveness of multiple-stripe analysis by using a stochastic earthquake input model

Scozzese, Fabrizio and Tubaldi, Enrico and Dall'Asta, Andrea (2020) Assessment of the effectiveness of multiple-stripe analysis by using a stochastic earthquake input model. Bulletin of Earthquake Engineering, 18 (7). pp. 3167-3203. ISSN 1573-1456

[img] Text (Scozzese-etal-BEE-2020-Assessment-of-the-effectiveness-of-multiple-stripe)
Scozzese_etal_BEE_2020_Assessment_of_the_effectiveness_of_multiple_stripe.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 16 March 2021.

Download (3MB) | Request a copy from the Strathclyde author

    Abstract

    Current practical approaches for probabilistic seismic performance assessment of structures rely on the concept of intensity measure (IM), which is used to decompose the problem into hazard analysis and conditional seismic demand analysis. These approaches are potentially more efficient than traditional Monte-Carlo based ones, but the performance estimates can be negatively influenced by inadequate setup choices. These include, among the others, the number of seismic intensity levels to consider, the number of structural analyses to be performed at each intensity level, and the lognormality assumption for the conditional demand. This paper investigates the accuracy and effectiveness of a widespread IM-based method for seismic performance assessment, multi-stripe analysis (MSA), through an extensive parametric study carried out on a three-story steel moment-resisting frame, by considering different setup choices and various engineering demand parameters. A stochastic ground motion model is employed to describe the seismic hazard and the spectral acceleration is used as intensity measure. The results of the convolution between the seismic hazard and the conditional probability of exceedance obtained via MSA are compared with the estimates obtained via Subset Simulation, providing a reference solution. The comparison gives useful insights on the influence of the main parameters controlling the accuracy and precision of the IM-based method. It is shown that, with the proper settings, MSA can provide risk estimates as accurate as those obtained via Subset Simulation, at a fraction of the computational cost.