Rapid Visual Screening of Buildings for Potential Seismic Hazards: Automated Deep-Learning Classification Approach
Shayan Shourabi, Ali Bakhshi · Journal of Computing in Civil Engineering · 2025
Rapid visual screening (RVS) is a way to assess unsafe structures and reduce urban earthquake vulnerability; which is labor-intensive, time-consuming, and costly through conventional methods. Given that screening relies on visual clues, this study has utilized deep-learning convolutional neural networks including Residual Network (ResNet), Visual Geometry Group (VGG), Inception, and Densely Connected Convolutional Networks (DenseNet) to extract and classify necessary visual features to perform, enhance, and provide a novel standard computer vision–based method, complete with image databases and a computer program, to automate this procedure. This approach addresses the limitations of previous research in direct screening applications. The algorithm achieved 61%, 64%, 80%, 85%, and 96% accuracy in extracting various features, which, due to task independence, do not compromise its overall robustness. Furthermore, wielding hierarchical classification resulted in a noteworthy 18% accuracy improvement in building type classification. The proposed program produces final scores in 53 s, making it 17 times faster than conventional methods. Combined with its compatibility with portable computers, this will significantly enhance the speed and efficiency of onsite procedures, drastically reducing labor costs and making large-scale projects more feasible. Lastly, the severely damaged buildings classifier is exploitable for postearthquake fast screening and identification.