Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks
Auteur(s): |
Pier Francesco Giordano
Said Quqa Maria Pina Limongelli |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Infrastructures, février 2021, n. 2, v. 6 |
Page(s): | 22 |
DOI: | 10.3390/infrastructures6020022 |
Abstrait: |
One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures. |
Copyright: | © 2021 the Authors. Licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10723106 - Publié(e) le:
22.04.2023 - Modifié(e) le:
10.05.2023