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

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A Publisher Correction to this article was published on 13 July 2020

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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.

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Authors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Eleonora M. Tronci & Maurizio De Angelis

  2. Columbia University, New York, USA

    Raimondo Betti

  3. Roma Tre University, Rome, Italy

    Vittorio Altomare

Authors
  1. Eleonora M. Tronci
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  2. Maurizio De Angelis
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  3. Raimondo Betti
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  4. Vittorio Altomare
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Corresponding authors

Correspondence to Maurizio De Angelis, Raimondo Betti or Vittorio Altomare.

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The original version of this article was revised: The conversion error occurred during the production process in usage of Greek characters has been corrected.

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Tronci, E.M., De Angelis, M., Betti, R. et al. Semi-Automated Operational Modal Analysis Methodology to Optimize Modal Parameter Estimation. J Optim Theory Appl 187, 842–854 (2020). https://doi.org/10.1007/s10957-020-01694-x

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  • DOI: https://doi.org/10.1007/s10957-020-01694-x

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