Abstract
A novel structural health assessment (SHA) technique is presented in this paper. It is a finite element-based time-domain nonlinear system identification technique. It can assess structural health at the element level using only a limited number of noise-contaminated responses and without using information on input excitation. It assesses the location and severity of defect(s) by tracking the changes in the stiffness properties of individual elements from their expected values. The procedure integrates an iterative least squares technique and the unscented Kalman filter (UKF) concept. The integrated procedure significantly improves the basic UKF concept. To demonstrate the effectiveness of the procedure, the health of a relatively large structural system under single and multiple excitations is assessed. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. The optimum number and location of measured responses are investigated. It is demonstrated that the method is capable of identifying defect-free and defective states of the structures using minimum information. Furthermore, it can locate defect spot within a defective element. It is also demonstrated that the proposed method, denoted as UKF-UI-WGI, is superior to the extended Kalman filter-based procedures for SHA developed by the team earlier.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 100-110 |
| Number of pages | 11 |
| Journal | Engineering Structures |
| Volume | 88 |
| DOIs | |
| State | Published - Apr 1 2015 |
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Keywords
- Damage detection
- Extended Kalman filter
- Nonlinear system identification
- Structural health assessment
- Substructure
- Unknown excitation
- Unscented Kalman filter
ASJC Scopus subject areas
- Civil and Structural Engineering
Cite this
Structural health assessment at a local level using minimum information. / Al-Hussein, Abdullah; Haldar, Achintya.
In: Engineering Structures, Vol. 88, 01.04.2015, p. 100-110.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Structural health assessment at a local level using minimum information
AU - Al-Hussein, Abdullah
AU - Haldar, Achintya
PY - 2015/4/1
Y1 - 2015/4/1
N2 - A novel structural health assessment (SHA) technique is presented in this paper. It is a finite element-based time-domain nonlinear system identification technique. It can assess structural health at the element level using only a limited number of noise-contaminated responses and without using information on input excitation. It assesses the location and severity of defect(s) by tracking the changes in the stiffness properties of individual elements from their expected values. The procedure integrates an iterative least squares technique and the unscented Kalman filter (UKF) concept. The integrated procedure significantly improves the basic UKF concept. To demonstrate the effectiveness of the procedure, the health of a relatively large structural system under single and multiple excitations is assessed. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. The optimum number and location of measured responses are investigated. It is demonstrated that the method is capable of identifying defect-free and defective states of the structures using minimum information. Furthermore, it can locate defect spot within a defective element. It is also demonstrated that the proposed method, denoted as UKF-UI-WGI, is superior to the extended Kalman filter-based procedures for SHA developed by the team earlier.
AB - A novel structural health assessment (SHA) technique is presented in this paper. It is a finite element-based time-domain nonlinear system identification technique. It can assess structural health at the element level using only a limited number of noise-contaminated responses and without using information on input excitation. It assesses the location and severity of defect(s) by tracking the changes in the stiffness properties of individual elements from their expected values. The procedure integrates an iterative least squares technique and the unscented Kalman filter (UKF) concept. The integrated procedure significantly improves the basic UKF concept. To demonstrate the effectiveness of the procedure, the health of a relatively large structural system under single and multiple excitations is assessed. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. The optimum number and location of measured responses are investigated. It is demonstrated that the method is capable of identifying defect-free and defective states of the structures using minimum information. Furthermore, it can locate defect spot within a defective element. It is also demonstrated that the proposed method, denoted as UKF-UI-WGI, is superior to the extended Kalman filter-based procedures for SHA developed by the team earlier.
KW - Damage detection
KW - Extended Kalman filter
KW - Nonlinear system identification
KW - Structural health assessment
KW - Substructure
KW - Unknown excitation
KW - Unscented Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=84923067188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84923067188&partnerID=8YFLogxK
U2 - 10.1016/j.engstruct.2015.01.026
DO - 10.1016/j.engstruct.2015.01.026
M3 - Article
AN - SCOPUS:84923067188
VL - 88
SP - 100
EP - 110
JO - Engineering Structures
JF - Engineering Structures
SN - 0141-0296
ER -