Semi-Automated Operational Modal Analysis Methodology to Optimize Modal Parameter Estimation

Eleonora M. Tronci (), Maurizio Angelis (), Raimondo Betti () and Vittorio Altomare ()
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Eleonora M. Tronci: Sapienza University of Rome
Maurizio Angelis: Sapienza University of Rome
Raimondo Betti: Columbia University
Vittorio Altomare: Roma Tre University

Journal of Optimization Theory and Applications, 2020, vol. 187, issue 3, No 11, 842-854

Abstract: Abstract Nowadays, long-term monitoring systems rely on the efficient implementation of automated methodologies to extract the modal parameters of buildings and bridges to assess their structural integrity. However, modal parameter estimation, usually, requires a certain level of user interaction, mainly when parametric system identification methods are used. Such procedures generally depend on the selection of a set of parameters, defined according to heuristic criteria, and kept constant during long monitoring campaigns. The main objective of this paper is to prove the necessity of abandoning identification approaches based on a single set of parameters for long-term monitoring campaigns and to propose a semi-automated modal identification tool, where the user-defined parameters vary within an established range of values, that can be set independently of the user’s expertise. The proposed method is validated with an application in the operational modal analysis of a historic civic tower, and its excellent results demonstrate the importance of considering multiple sets of parameters, mainly when dealing with complex structures and challenging monitoring conditions.

Keywords: Semi-automated system identification; Subspace stochastic identification; Parametric system identification; Unsupervised learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-020-01694-x

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