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Damage identification scheme based on compressive sensing

Wang, Ying and Hao, Hong 2015, Damage identification scheme based on compressive sensing, Journal of computing in civil engineering, vol. 29, no. 2, Article Number : 04014037, pp. 1-10, doi: 10.1061/(ASCE)CP.1943-5487.0000324.

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Title Damage identification scheme based on compressive sensing
Author(s) Wang, Ying
Hao, Hong
Journal name Journal of computing in civil engineering
Volume number 29
Issue number 2
Season Article Number : 04014037
Start page 1
End page 10
Total pages 10
Publisher American Society of Civil Engineers (ASCE)
Place of publication New York, N.Y.
Publication date 2015-03
ISSN 0887-3801
Keyword(s) Civil infrastructure
Compressive sensing
Damage identification
Pattern recognition
Sparse representation
Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Engineering, Civil
Computer Science
Engineering
Infrastructure
Automatic identification systems
Damage
Probe instruments
STATISTICAL PATTERN-RECOGNITION
UNCERTAINTY PRINCIPLES
ATOMIC DECOMPOSITION
NEURAL-NETWORK
MODEL
Summary Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.
Language eng
DOI 10.1061/(ASCE)CP.1943-5487.0000324
Field of Research 090506 Structural Engineering
090609 Signal Processing
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, ASCE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076171

Document type: Journal Article
Collection: School of Engineering
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