TY - JOUR

T1 - Gaussian and non-Gaussian stochastic sensitivity analysis of discrete structural system

AU - Benfratello, Salvatore

AU - Caddemi, null

AU - Muscolino, null

AU - Muscolino, Giuseppe Alfredo

AU - Caddemi, Salvatore

PY - 2000

Y1 - 2000

N2 - The derivatives of the response of a structural system with respect to the system parameters are termed sensitivities. They play an important role in assessing the effect of uncertainties in the mathematical model of the system and in predicting changes of the response due to changes of the design parameters. In this paper, a time domain approach for evaluating the sensitivity of discrete structural systems to deterministic, as well as to Gaussian or non-Gaussian stochastic input is presented. In particular, in the latter case, the stochastic input has been assumed to be a delta-correlated process and, by using Kronecker algebra extensively, cumulant sensitivities of order higher than two have been obtained by solving sets of algebraic or differential equations for stationary and non-stationary input, respectively. The theoretical background is developed for the general case of multi-degrees-of-freedom (MDOF) primary system with an attached secondary single-degree-of-freedom (SDOF) structure. However, numerical examples for the simple case of an SDOF primary-secondary structure, in order to explore how variations of the system parameters influence the system, are presented. Finally, it should be noted that a study of the optimal placement of the secondary system within the primary one should be conducted on an MDOF structure.

AB - The derivatives of the response of a structural system with respect to the system parameters are termed sensitivities. They play an important role in assessing the effect of uncertainties in the mathematical model of the system and in predicting changes of the response due to changes of the design parameters. In this paper, a time domain approach for evaluating the sensitivity of discrete structural systems to deterministic, as well as to Gaussian or non-Gaussian stochastic input is presented. In particular, in the latter case, the stochastic input has been assumed to be a delta-correlated process and, by using Kronecker algebra extensively, cumulant sensitivities of order higher than two have been obtained by solving sets of algebraic or differential equations for stationary and non-stationary input, respectively. The theoretical background is developed for the general case of multi-degrees-of-freedom (MDOF) primary system with an attached secondary single-degree-of-freedom (SDOF) structure. However, numerical examples for the simple case of an SDOF primary-secondary structure, in order to explore how variations of the system parameters influence the system, are presented. Finally, it should be noted that a study of the optimal placement of the secondary system within the primary one should be conducted on an MDOF structure.

KW - Civil and Structural Engineering

KW - Computer Science Applications1707 Computer Vision and Pattern Recognition

KW - Materials Science (all)

KW - Mechanical Engineering

KW - Modeling and Simulation

KW - Civil and Structural Engineering

KW - Computer Science Applications1707 Computer Vision and Pattern Recognition

KW - Materials Science (all)

KW - Mechanical Engineering

KW - Modeling and Simulation

UR - http://hdl.handle.net/10447/285111

M3 - Article

VL - 78

SP - 425

EP - 434

JO - COMPUTERS & STRUCTURES

JF - COMPUTERS & STRUCTURES

SN - 0045-7949

ER -