Categorization of Post-Earthquake Damages in RC Structural Elements with Deep Learning Approach

YILMAZ M., Dogan G., Arslan M. H., İlki A.

Journal of Earthquake Engineering, vol.28, no.9, pp.2620-2651, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 9
  • Publication Date: 2024
  • Doi Number: 10.1080/13632469.2024.2302033
  • Journal Name: Journal of Earthquake Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2620-2651
  • Keywords: convolutional neural network, Damage, damage assessment, DamageNet, earthquake
  • Erciyes University Affiliated: Yes

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 Kahramanmaraş/Turkey and classification success of 88% was obtained.