Explainable Ensemble Convolutional Neural Networks for Automated Post-Disaster Structural Damage Assessment

Abstract

The recent seismic activity in southeastern Turkey in February 2023 again emphasized the critical need to promptly evaluate structural damage to assist in emergency response operations. This study introduces a comprehensive ensemble deep learning approach to structural damage classification following earthquake events, based on a dataset containing 13,270 high-resolution images with 15 different damage classes. Six different state-of-the-art convolutional neural network models (VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2) are combined using a weighted voting approach to handle extreme class imbalance using weighted categorical cross-entropy loss. An integrated explainability component is incorporated into the trained convolutional neural network models to highlight the image regions that contribute to the predicted damage class, thereby improving the interpretability of deep learning decisions in safety-critical post-disaster assessment scenarios. The performance evaluation results show that the ensemble model achieves a test accuracy of 93.77%, with an increase of 2.67% compared to the best performing model individually. Notably, the ensemble model improves performance in minority classes like collapsed buildings. The proposed framework can be used to provide a powerful approach to structural damage evaluation, balancing accuracy with interpretability, to assist structural engineers in post-earthquake evaluation procedures.