Damage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometers

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dc.authoridHIZAL, Çağlayan/0000-0002-9783-6511
dc.authoridAVCI, Muhammed Serdar/0000-0003-0884-3734
dc.contributor.authorErcan, Emre
dc.contributor.authorAvci, Muhammed Serdar
dc.contributor.authorPekedis, Mahmut
dc.contributor.authorHizal, Caglayan
dc.date.accessioned2024-08-31T07:50:28Z
dc.date.available2024-08-31T07:50:28Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractStructural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.en_US
dc.identifier.doi10.3390/app14062628
dc.identifier.issn2076-3417
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85192477881en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app14062628
dc.identifier.urihttps://hdl.handle.net/11454/105241
dc.identifier.volume14en_US
dc.identifier.wosWOS:001191572500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240831_Uen_US
dc.subjectStructural Health Monitoringen_US
dc.subjectVibration-Based Methodsen_US
dc.subjectDamage Detectionen_US
dc.subjectConvolutional Neural Networks (Cnns)en_US
dc.titleDamage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometersen_US
dc.typeArticleen_US

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