Damage Detection and Localization in Structures: A Statistics Based Algorithm Using a Densely Clustered Sensor Network
Damage prognosis for structural health monitoring is a challenging and complex research topic in civil engineering. Early and accurate damage detection is essential to maximizing the useful life of structures. The use of densely clustered sensor networks provides promising applications in the analysis of structural components and identification of local damage. The proposed localized damage detection method utilizes a linear regression analysis to monitor changes in the linear behavior of a structure with the onset of damage. The structural responses at various sensor locations along a structure are compared to those of other locations and pair-wise influence coefficients are estimated. These coefficients serve as damage indicators when damaged values are compared to healthy-state values. By statistically comparing the change in influence coefficients, structural damage can be accurately and effectively identified. The method is verified using simulations and an experimental prototype of a local beam-column connection, as well as a simulated model of a two-span bridge girder.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411711
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Supplemental Notes:
- Copyright © 2011 ASCE
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Authors:
- Labuz, Elizabeth L
- Pakzad, Shamim N
- Cheng, Liang
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0000-0002-1615-9169
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Conference:
- 2011 Structures Congress
- Location: Las Vegas NV, United States
- Date: 2011-4-14 to 2011-4-16
- Publication Date: 2011
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
Subject/Index Terms
- TRT Terms: Algorithms; Beams; Columns; Data collection; Evaluation; Information processing; Sensors; Structural health monitoring
- Subject Areas: Bridges and other structures; Data and Information Technology; Maintenance and Preservation; I20: Design and Planning of Transport Infrastructure; I60: Maintenance;
Filing Info
- Accession Number: 01347690
- Record Type: Publication
- ISBN: 978-0-7844-1171-1
- Files: TRIS, ASCE
- Created Date: Aug 8 2011 2:19PM