چکیده مقاله
Rapid visual screening RVS is crucial for identifying potentially unsafe structures and mitigating urban earthquake vulnerability, but conventional methods are labor intensive, time consuming, costly, and susceptible to subjective errors due to human involvement in the process This study introduces a novel automated deep learning DL approach, leveraging convolutional neural networks CNNs , including residual network ResNet , visual geometry group VGG , inception, and densely connected convolutional networks DenseNet The methodology employs hard voting ensemble methods and hierarchical classification to extract and classify necessary visual features from street view images for RVS This approach addresses the limitations of previous research in direct screening applications The developed program significantly accelerates the process, producing final scores in just 53 seconds, making it 17 times faster than conventional methods, with accuracies up to 96% This approach drastically reduces labor costs, enhances feasibility for large scale projects, and offers a robust tool for both pre earthquake evaluation and post earthquake rapid screening of damaged structures, thereby improving urban earthquake resilience
کلیدواژهها
نویسندگان
Shayan Shourabi
M Sc Student, Department of Civil Engineering, Sharif University of Technology, Azadi Avenue, P O Box ۱۱۳۶۵ ۹۳۱۳, Tehran, Iran
علی بخشی
Professor, Department of Civil Engineering, Sharif University of Technology, Azadi Avenue, P O Box ۱۱۳۶۵ ۹۳۱۳, Tehran, Iran
شیوه ارجاع
Shourabi, Shayan and Bakhshi, Ali,1404,Deep Learning Approach for Automated Rapid Visual Screening,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران