Post-Earthquake Assessment of Buildings Using Deep Learning
This work addresses post-earthquake safety and repair by providing an automated damage assessment tool, but it is incremental as it applies an existing method to a specific domain.
The authors tackled the problem of classifying building damage after earthquakes using a CNN-based model, achieving a validation accuracy of 89.38% with VGG16 transfer learning.
Classification of the extent of damage suffered by a building in a seismic event is crucial from the safety perspective and repairing work. In this study, authors have proposed a CNN based autonomous damage detection model. Over 1200 images of different types of buildings-1000 for training and 200 for testing classified into 4 categories according to the extent of damage suffered. Categories are namely, no damage, minor damage, major damage, and collapse. Trained network tested by the application of various algorithms with different learning rates. The most optimum results were obtained on the application of VGG16 transfer learning model with a learning rate of 1e-5 as it gave a training accuracy of 97.85% and validation accuracy of up to 89.38%. The model developed has real-time application in the event of an earthquake.