Estimation of Damage Levels in Masonry Structures Following Earthquake Impact Using Deep Learning-based Segmentation Method

This study presents an innovative and automated methodology for assessing damage levels in masonry structures following earthquakes, utilizing a deep learning-based segmentation approach. Central to this research is the use of a U-Net convolutional neural network (CNN) model, which facilitates automated damage detection with a focus on smartphone-enabled, real-time analysis. A key feature of this method is a novel damage index (DI), calculated by normalizing the surface area of detected damages against the total area of the structure, as viewed from the same perspective. The findings indicate a marked improvement in damage detection capabilities, with the U-Net model achieving a precision of approximately 92% and a recall of around 93%. These figures highlight the model's proficiency in accurately identifying damaged areas and reducing false positives, an essential aspect of post-earthquake evaluations. While this methodology represents a significant step forward in enabling rapid and cost-effective post-earthquake inspections, it is accompanied by certain constraints, particularly in terms of dataset diversity and computational requirements. Despite these challenges, the high accuracy and effectiveness of the damage detection and indexing process demonstrate strong potential for future applications in structural health monitoring, especially in scenarios that demand prompt action and are limited by resources.