Deep Learning-Based Anomaly Detection to Classify Inaccurate Data and Damaged Condition of a Cable-Stayed Bridge

Deep Learning-Based Anomaly Detection to Classify Inaccurate Data and Damaged Condition of a Cable-Stayed Bridge

Cables of cable-stayed bridges are gradually damaged by weather conditions, vehicle loads, and corrosion of materials. Stayed cables are an essential factor closely related to the stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to tension capacity lost. There...

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Bibliographic Details
Main Authors: Hyesook Son, Yun Jang, Seung-Eock Kim, Dongjoo Kim, Jong-Woong Park
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
SHM
LSTM
anomaly detection
time series
deep learning
Online Access:https://ieeexplore.ieee.org/document/9497115/

Internet

https://ieeexplore.ieee.org/document/9497115/

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