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Detecting changes in boundary conditions based on sensitivity-based statistical tests

  • Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables aStructural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliabilitybased approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data and a model from the undamaged structure, in an unsupervised learning mode, making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.zeige mehrzeige weniger

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Metadaten
Autor*innen:A. Mendler, M. Döhler, Falk Hille
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):E-journal of nondestructive testing
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:NDT.net
Verlagsort:Kirchwald
Jahrgang/Band:27
Ausgabe/Heft:9
Erste Seite:1
Letzte Seite:13
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Asymptotic local approach; Global ambient vibrations; Nelson’s method; Probability of detection; Sensitivity vectors; Stress stiffening
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Veranstaltung:International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE 2022)
Veranstaltungsort:Zurich, Switzerland
Beginndatum der Veranstaltung:16.08.2022
Enddatum der Veranstaltung:18.08.2022
URN:urn:nbn:de:kobv:b43-566632
URL:https://www.ndt.net/?id=27311
ISSN:1435-4934
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:21.12.2022
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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