Baensch, Franziska
[VerfasserIn];
Brunner, A. J.
[VerfasserIn]
Towards predictor development for assessing structural integrity of components made from wood materials using Acoustic Emission monitoring and signal analysis
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Medientyp:
Elektronischer Konferenzbericht
Titel:
Towards predictor development for assessing structural integrity of components made from wood materials using Acoustic Emission monitoring and signal analysis
Beteiligte:
Baensch, Franziska
[VerfasserIn];
Brunner, A. J.
[VerfasserIn]
Erschienen:
BAM-Publica - Publikationsserver der Bundesanstalt für Materialforschung und -prüfung (BAM), 2019
Sprache:
Englisch
ISBN:
978-3-947971-07-7
Entstehung:
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
Against the background of sustainable resource management and efficiency, wood-based materials are currently experiencing a revival and, among others, plywood, Laminated Veneer Lumber and glued laminated timber are becoming increasingly more important in the building sector. Even though these materials are so-called engineered products, the element wood is naturally grown with intrinsic variability in mechanical properties and requires professional handling on-site. Otherwise, load-bearing structures made of wood materials may entail certain risks. Critical situations can, in principle, be avoided by implementing a structural health monitoring system into components or structures made from wood material. The aim is to indicate accumulation of mechanical damage and to eliminate or at least significantly reduce the risk of unexpected failure. Toward this purpose, the failure behavior of several layered wood materials under quasi-static tension was investigated in laboratory-scale experiments by means of acoustic emission (AE) measurement. Based on spectral analysis and pattern recognition, two classes of AE signals are identified for each investigated lay-up that are characterized by either low or high frequency contents in the respective power spectra. AE activity and intensity of both signal classes are analyzed, striving for predictors appropriate for AE monitoring concepts.