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• W4309046930 endingPage "103385" @default.
• W4309046930 startingPage "103385" @default.
• W4309046930 abstract "This paper develops a reliability assessment method for dynamic systems subjected to a general random process excitation. Safety assessment using direct Monte Carlo simulation is computationally expensive, particularly when estimating low probabilities of failure. The Girsanov transformation-based reliability assessment method is a computationally efficient approach intended for dynamic systems driven by Gaussian white noise, and this approach can be extended to random process inputs that can be represented as transformations of Gaussian white noise. In practice, dynamic systems may be subjected to inputs that may be better modeled as non-Gaussian and/or non-stationary random processes, which are not easily transformable to Gaussian white noise. We propose a computationally efficient scheme, based on importance sampling, which can be implemented directly on a general class of random processes — both Gaussian and non-Gaussian, and stationary and non-stationary. We demonstrate that this approach is in fact equivalent to Girsanov transformation when the uncertain inputs are Gaussian white noise processes. The proposed approach is demonstrated on a linear dynamic system driven by Gaussian white noise and Brownian bridge processes, a multi-physics aero-thermo-elastic model of a flexible panel subjected to hypersonic flow, and a nonlinear building frame subjected to non-stationary non-Gaussian random process excitation." @default.
• W4309046930 created "2022-11-21" @default.
• W4309046930 creator A5072393639 @default.
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• W4309046930 date "2023-01-01" @default.
• W4309046930 modified "2023-09-27" @default.
• W4309046930 title "Importance sampling for reliability assessment of dynamic systems under general random process excitation" @default.
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• W4309046930 doi "https://doi.org/10.1016/j.probengmech.2022.103385" @default.
• W4309046930 hasPublicationYear "2023" @default.
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