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https://hdl.handle.net/20.500.13091/5130
Title: | Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach | Authors: | Yılmaz, Mertcan Doğan, Gamze Arslan, Musa Hakan Ilki, Alper |
Keywords: | Damage damage assessment earthquake convolutional neural network DamageNet Neural-Network Prediction Earthquake Buildings |
Publisher: | Taylor & Francis Ltd | 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. | URI: | https://doi.org/10.1080/13632469.2024.2302033 https://hdl.handle.net/20.500.13091/5130 |
ISSN: | 1363-2469 1559-808X |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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