Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4548
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dc.contributor.authorGhadge, Prajakta-
dc.contributor.authorGuchhait, Shyamal-
dc.contributor.authorDas, Tanmay-
dc.date.accessioned2024-04-24T04:45:44Z-
dc.date.available2024-04-24T04:45:44Z-
dc.date.issued2024-04-
dc.identifier.citationInternational Conference on Advanced Technology in Material Science and Engineering (ICATMSE-24), ITER, Shiksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India, 5-6 April 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4548-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractHealth monitoring of structures and detecting structural damages are crucial for maintaining structural integrity and assuring desirable service life. Traditionally, damage detection has been carried out through visual inspection, but this method has several limitations. Therefore, Vibration-based damage detection has become a more effective substitute for traditional techniques. The core idea is that the existence of damage or weakening in a structure leads to alterations in its dynamic characteristics. This method can be applied using changes in different dynamic properties like natural frequency, mode shapes, and their derivatives. Artificial Intelligence (AI) is being extensively used in this field to detect damage with greater accuracy because of its useful features. This study proposes a deep learning (DL) based damage detection technique for damage analysis of plate-like structures with modal data. The plate structure is simulated with the FE analysis software ABAQUS, and modal data is collected. Noisy synthetic data is generated by adding different quantities of Gaussian random noise to the modal data. The 1D-CNN algorithm is utilized here to perform damage localization and quantification using noisy synthetic data as input. The proposed damage detection method has proved to be highly effective in detecting damage location and severity of plate structure.en_US
dc.subjectStructural Health Monitoring (SHM)en_US
dc.subjectModal analysis of plateen_US
dc.subjectDamage detectionen_US
dc.subject1D-CNNen_US
dc.titleDamage Detection of Plate Structure with 1D Convolutional Neural Network (1D-CNN) Using Modal Dataen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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