Makale detayı · 2024
Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach
Journal of Earthquake Engineering
- Yıl
- 2024
- ISSN
1363-2469- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
İngilizce (OpenAlex)
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 Kahramanmaraş/Turkey and classification success of 88% was obtained.
Konular
- Infrastructure Maintenance and Monitoring
- Concrete Corrosion and Durability
- Structural Health Monitoring Techniques
Birincil konu Infrastructure Maintenance and Monitoring