Deep Learning-based Post-earthquake Structural Damage Classification with Small Datasets

This paper introduces a deep learning-based approach for automated classification of local structural element failure modes in post-earthquake buildings using image-based data. Addressing the critical challenge of limited training datasets in the structural/earthquake engineering domain, targeted, domain-informed data augmentation and synthetic data generation techniques are proposed to enhance dataset size and diversity. The model architecture and preprocessing pipeline are explicitly designed to capture damage-sensitive features in images that are essential for informed decision-making on structural integrity of the building, thus extending beyond conventional classification tasks. Dataset enhancement, transfer learning and model regularisation techniques are integrated to ensure alignment of model predictions with expert domain judgement. Achieving 0.93 (93%) accuracy, precision, recall and F1-score, the developed model exhibits robust generalisability without overfitting, demonstrating clear potential for practical deployment in disaster resilience and infrastructure recovery efforts.

Presented at combined conference: IVPRICIEV2025 (Kitakyushu, Japan), 26 -29 May 2025