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dc.contributor.authorHuang, Yuxin
dc.contributor.authorMochida, Yusuke
dc.date.accessioned2024-07-01T02:01:15Z
dc.date.available2024-07-01T02:01:15Z
dc.date.issued2024-04-09
dc.identifier.urihttps://repo.nzsee.org.nz/xmlui/handle/nzsee/2725
dc.description.abstractStructural Damage Detection methods are crucial for infrastructure maintenance. The general principle involves the identification of changes in natural frequencies induced by stiffness reductions. This paper introduces an innovative non-contact approach to Structural Damage Detection, using acoustic methods through microphone recording, which offers both convenience and cost-effectiveness when compared to traditional contact and optical techniques. The paper aims to assess the feasibility of this method in terms of damage severity detection, damage location identification, and overall accuracy through experimental testing. In a simulated free beam scenario with I-beam specimens, various damage severities (intact, 10%, 25%, and 50% depth saw cut) at two locations (middle and quarter span) are tested by analyzing the first three mode frequencies on the strong axis. Results from the proposed microphone method are compared to the accelerometer method and ANSYS simulations. Impact factors such as knocking locations, sensor placement, and microphone distance to the beam are also considered. Results demonstrate a clear reduction in frequencies as damage severity increases, affirming the feasibility of the proposed method for detecting structural damage. The microphone-based results closely match those obtained from accelerometers, underscoring its accuracy. This outcome serves as a foundational step and bolsters confidence in the continued development of innovative non-contact Structural Damage Detection technology. Such advancements can be applied to a broader spectrum of structural conditions, addressing diverse market needs.
dc.language.isoen
dc.publisherNew Zealand Society for Earthquake Engineering
dc.relation.ispartofseries2024;151
dc.subjectDigital tools, machine learning (AI) and emerging technologies in earthquake engineering
dc.titleNon-contact Structural Damage Detection by Natural Frequency Measurement using Microphone
dc.typeArticle


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