Data-driven and physics-based interval modelling of power spectral density functions from limited data

Behrendt, Marco, Dang, Chao and Beer, Michael ORCID: 0000-0002-0611-0345 (2024) Data-driven and physics-based interval modelling of power spectral density functions from limited data. Mechanical Systems and Signal Processing, 208. p. 111078.

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2023.111078

Abstract

In stochastic dynamics, ensuring the structural reliability of buildings and structures is of paramount importance, especially when subjected to environmental loads such as wind or earthquakes. To adequately address these loads and the uncertainties associated with them, it is often necessary to utilise advanced load models, frequently expressed using a power spectral density (PSD) function. The construction of these load models becomes challenging when only limited data is available and meaningful statistics cannot be reliably derived. To address this issue, safety bounds are commonly used in load models to account for uncertainties. Many PSD functions, such as the Clough–Penzien model, are described by parameters with a physical background and can therefore reflect the real case. The aim of this work is to expand these physical parameters in order to account for uncertainties. For this purpose, bootstrapping is used to derive more reliable statistics. By introducing a scaling parameter that allows for flexibility, bounds of the data set can be derived. Consequently, suitable PSD models are fitted to the derived bounds. The PSD function is thus represented by intervals for its physical properties instead of relying on discrete values. When applying such a bounded load model to a structure, advanced interval propagation schemes can be utilised to bound the failure probability.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	09 Jan 2024 10:29
Last Modified:	03 Feb 2024 06:22
DOI:	10.1016/j.ymssp.2023.111078
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177776