A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications
Joint Authors
Ye, Xiao-Wei
Dong, C. Z.
Liu, T.
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM).
This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring.
A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed.
Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling.
The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.
American Psychological Association (APA)
Ye, Xiao-Wei& Dong, C. Z.& Liu, T.. 2016. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications. Journal of Sensors،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1110582
Modern Language Association (MLA)
Ye, Xiao-Wei…[et al.]. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications. Journal of Sensors No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1110582
American Medical Association (AMA)
Ye, Xiao-Wei& Dong, C. Z.& Liu, T.. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1110582
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110582