Sessa, Salvatore (2008) Application of the Tail-Equivalent Linearization Method for Stochastic Dynamic Analysis with Asymmetric Hysteresis. [Tesi di dottorato] (Unpublished)

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Item Type: Tesi di dottorato
Resource language: English
Title: Application of the Tail-Equivalent Linearization Method for Stochastic Dynamic Analysis with Asymmetric Hysteresis
Creators:
Creators
Email
Sessa, Salvatore
sal.sessa@gmail.com
Date: 30 November 2008
Number of Pages: 178
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria dei materiali e della produzione
Dottorato: Ingegneria dei materiali e delle strutture
Ciclo di dottorato: 21
Coordinatore del Corso di dottorato:
nome
email
Acierno, Domenico
UNSPECIFIED
Coordinatore del Corso di dottorato (extra):
nome
email
Prota, Andrea
UNSPECIFIED
Tutor:
nome
email
Rosati, Luciano
rosati@unina.it
Date: 30 November 2008
Number of Pages: 178
Keywords: Nonlinear Random Vibrations Analysis
Settori scientifico-disciplinari del MIUR: Area 08 - Ingegneria civile e Architettura > ICAR/09 - Tecnica delle costruzioni
Area 08 - Ingegneria civile e Architettura > ICAR/08 - Scienza delle costruzioni
Date Deposited: 13 Nov 2009 14:07
Last Modified: 09 Dec 2014 10:09
URI: http://www.fedoa.unina.it/id/eprint/3329

Collection description

The tail-equivalent linearization method (TELM) is used to investigate the stationary response of a system having a highly asymmetric hysteretic behavior and subjected to a discretized white-noise excitation. The equivalent linear system is defined by equating its tail probability with the first-order approximation of the tail probability of the nonlinear response. The equivalent linear system is determined in a non-parametric form in terms of its impulse response function, which depends on the response threshold of interest. The method is able to capture the non-Gaussian and asymmetric distribution of both the point-in-time response and the extreme response over a time interval (the first-passage probability) for large thresholds (small exceedance probabilities), which are of interest in reliability analysis.

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