Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5130
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yılmaz, Mertcan | - |
dc.contributor.author | Doğan, Gamze | - |
dc.contributor.author | Arslan, Musa Hakan | - |
dc.contributor.author | Ilki, Alper | - |
dc.date.accessioned | 2024-02-16T14:42:20Z | - |
dc.date.available | 2024-02-16T14:42:20Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1363-2469 | - |
dc.identifier.issn | 1559-808X | - |
dc.identifier.uri | https://doi.org/10.1080/13632469.2024.2302033 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/5130 | - |
dc.description.abstract | The aim of this study was to develop an innovative deep learning based intelligent software (DamageNet) and its mobile applications to classify seismic damage of Reinforced Concrete (RC) elements. Images of 2455 damaged elements that have been exposed to different destructive earthquakes were collected from the datacenterhub database. The DamageNet algorithm has been compared with the pretrained convolutional neural networks (CNN) algorithms (VGG16, ResNet-50, MobileNetV2 and EfficientNet) according to performance metrics. With the other models, a maximum test success of 89% was achieved, while with DamageNet a test success of 92% was achieved in damage classification. The mobile application developed based on the DamageNet model was tested in the field after the earthquakes (Mw:7.7 and Mw:7.6) in Kahramanmaras/Turkey and classification success of 88% was obtained. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis Ltd | en_US |
dc.relation.ispartof | Journal of Earthquake Engineering | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Damage | en_US |
dc.subject | damage assessment | en_US |
dc.subject | earthquake | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | DamageNet | en_US |
dc.subject | Neural-Network | en_US |
dc.subject | Prediction | en_US |
dc.subject | Earthquake | en_US |
dc.subject | Buildings | en_US |
dc.title | Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach | en_US |
dc.type | Article | en_US |
dc.type | Article; Early Access | en_US |
dc.identifier.doi | 10.1080/13632469.2024.2302033 | - |
dc.identifier.scopus | 2-s2.0-85182709000 | en_US |
dc.department | KTÜN | en_US |
dc.authorid | Yilmaz, Mertcan/0000-0001-9868-2991 | - |
dc.identifier.wos | WOS:001145214900001 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 58820565300 | - |
dc.authorscopusid | 57191169845 | - |
dc.authorscopusid | 11940766700 | - |
dc.authorscopusid | 6603045524 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.openairetype | Article; Early Access | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | 02.02. Department of Civil Engineering | - |
crisitem.author.dept | 02.02. Department of Civil Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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