Damage Scenario Prediction for Concrete Bridge Columns Using Deep Generative Networks
Journal
Structural Control and Health Monitoring
Journal Volume
2024
Journal Issue
1
Start Page
5526537
ISSN
1545-2255
1545-2263
Date Issued
2024-01
Author(s)
Tzu-Kang Lin
Hao-Tun Chang
Ping-Hsiung Wang
Ahmed Abdalfatah Saddek
Dzong-Chwang Dzeng
DOI
10.1155/2024/5526537
Abstract
Bridges in areas with high seismic risk are constantly exposed to earthquake threats. Therefore, comprehensive bridge damage assessments are essential for postearthquake retrofitting and safety assurance. However, traditional methods of assessing damage and collecting data are time-consuming and labor-intensive. To address this issue, this study proposes a deep generative adversarial network (GAN)-based approach to predict the surface damage patterns of bridge columns. Using visual patterns from experimental tests, the proposed approach can generate surface damage to the synthetic column, such as cracks and concrete splinters. The study also investigates the effects of different data representation schemes, such as grayscale, black and white, and obstacle-removed images, and uses the corresponding damage indices as additional constraints to improve network training. The results show that the proposed approach can offer a reliable reference for bridge engineers to evaluate and repair seismic-induced bridge damage, which can significantly lower the cost of disaster reconnaissance.
Publisher
Wiley
Type
journal article