A new stochastic simulation algorithm for updating robust reliability of linear structural dynamic systems subjected to future Gaussian excitations

In this paper, we are interested in using system response data to update the robust failure probability that any particular response of a linear structural dynamic system exceeds a specified threshold during the time when the system is subjected to future Gaussian dynamic excitations. Computation of...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 326; pp. 481 - 504
Main Authors: Bansal, Sahil, Cheung, Sai Hung
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.11.2017
Elsevier BV
Subjects:
Bayesian analysis
Computer simulation
Dynamical systems
Failure
Mathematical models
Modal data
Model updating
Normal distribution
Parameter uncertainty
Probability theory
Random variables
Reliability
Reliability engineering
Reliability updating
Robustness (mathematics)
Sampling
Simulation
Stochastic simulation
Structural reliability
Studies
Uncertainty quantification
Upgrading
Uncertainty quantification
Structural reliability
Stochastic simulation
Reliability updating
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary:In this paper, we are interested in using system response data to update the robust failure probability that any particular response of a linear structural dynamic system exceeds a specified threshold during the time when the system is subjected to future Gaussian dynamic excitations. Computation of the robust reliability takes into account uncertainties from structural modeling in addition to the modeling of the uncertain excitations that the structure will experience during its lifetime. In partial, modal data from the structure are used as the data for the updating. By exploiting the properties of linear dynamics, a new approach based on stochastic simulation methods is proposed to update the robust reliability of the structure. The proposed approach integrates the Gibbs sampler for Bayesian model updating and Subset Simulation for failure probability computation. A new efficient approach for conditional sampling called ‘Constrained Metropolis within Gibbs sampling’ algorithm is developed by the authors. It is robust to the number of uncertain parameters and random variables and the dimension of modal data involved in the problem. The effectiveness and efficiency of the proposed approach are illustrated by two numerical examples involving linear elastic dynamic systems. •Literature establishes motives behind interest in Robust reliability updating.•Structural modeling and stochastic excitation modeling uncertainties are considered.•Approach is robust to the number of random variables and the dimension of modal data.•New algorithm is proposed to simulate samples from conditional distribution.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2017.07.032