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Effects of stochastic generation on the elastic and inelastic spectra of fully non-stationary accelerograms

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journal contribution
posted on 2022-12-06, 09:06 authored by Federica Genovese, Giuseppe Muscolino, Alessandro Palmeri

Nonlinear dynamic analyses are a state-of-the-art tool to assess the performance of earthquake-resistant structures. Inevitably, the validity of the predicted seismic response depends on the fidelity of the computational model to the actual structural behavior and the representativeness of the time histories of ground acceleration as realizations of the seismic hazard for the site under consideration. The generation of artificial time histories is generally allowed by international seismic codes and represents a valid alternative to recorded accelerograms, provided that the key features in the expected seismic input are preserved in the generated signals. Different stochastic generation methods of fully non-stationary accelerograms have been proposed in the literature. Two alternative randomization strategies are compared in this paper, based on (i) wavelets analysis and (ii) evolutionary power spectral density (PSD) functions. The analyses are focused on the aleatory variability observed in the generated elastic and inelastic response spectra in relation to different modelling choices, offering qualitative and quantitative information to designers using stochastically generated accelerograms.

History

School

  • Architecture, Building and Civil Engineering

Published in

Probabilistic Engineering Mechanics

Volume

71

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Probabilistic Engineering Mechanics and the definitive published version is available at https://doi.org/10.1016/j.probengmech.2022.103377

Acceptance date

2022-10-28

Publication date

2022-11-07

Copyright date

2022

ISSN

0266-8920

Language

  • en

Depositor

Dr Alessandro Palmeri. Deposit date: 30 November 2022

Article number

103377