TY - JOUR
T1 - Prognostics and Structural Health Assessment Using Uncertain Measured Response Information
AU - Haldar, Achintya
AU - Al-Hussein, Abdullah
N1 - Funding Information:
Acknowledgments The authors would like to thank all the team members for their help in developing the overall research concept of structural health assessment and monitoring. They include Drs. Duan Wang, Peter H. Vo, Xiaolin Ling, Hasan N. Katkhuda, Rene Martinez-Flores, Ajoy K. Das, Mr. J. P. Kazakoff and A. R. Safdar. The team received financial supports from the National Science Foundation, a small grant from the University of Arizona, graduate student supports from various sources including Raytheon Missile Corporation, Ministry of Higher Education, Jordon, CONACYT, Iraq’s Ministry of Higher Education and Scientific Research, Department of Civil Engineering and Engineering Mechanics, University of Arizona, etc. The financial supports from the National Science Foundation; most recently under Grant No. CMMI-1403844 is also appreciated. Any opinions, findings, or recommendations expressed in this paper are those of the writers and do not necessarily reflect the views of the sponsors.
Publisher Copyright:
© 2016, Springer International Publishing Switzerland.
PY - 2016
Y1 - 2016
N2 - The authors and their team members have been working on developing implementable techniques for the objective rapid assessment of structural health (RASH) just after major natural and man-made events or in the context of maintenance over a period of time. They used the system-identification techniques by eliminating some of its weaknesses. For easier implementation, the excitation information was completely ignored. To locate defects and their severity at the local element level, the structures were represented by finite elements. By tracking the changes in the stiffness parameters of each element, the location(s) and severity of defects are assessed. The team conducted extensive analytical and laboratory investigations to verify all the methods. They had to overcome several challenges related to the conceptual and analytical development, data processing, and the presence of uncertainty in the every phase. To consider nonlinearity in the system identification process, a method known as Generalized Iterative Least Squares-Extended Kalman Filter-Unknown Input (GLIS-EKF-UI), was developed earlier. Since it failed to identify structures in some cases, the authors recently proposed a new method denoted as Unscented Kalman Filter—Unknown Input- Weighted Global Iterations (UKF-UI-WGI). With the help of informative examples, the superiority of UKF-UI-WGI over GLIS-EKF-UI is documented in this paper. Since at the beginning of an inspection, the defects and their severity are expected to be unknown, the authors recommend UKF-UI-WGI for the rapid assessment of health of infrastructures.
AB - The authors and their team members have been working on developing implementable techniques for the objective rapid assessment of structural health (RASH) just after major natural and man-made events or in the context of maintenance over a period of time. They used the system-identification techniques by eliminating some of its weaknesses. For easier implementation, the excitation information was completely ignored. To locate defects and their severity at the local element level, the structures were represented by finite elements. By tracking the changes in the stiffness parameters of each element, the location(s) and severity of defects are assessed. The team conducted extensive analytical and laboratory investigations to verify all the methods. They had to overcome several challenges related to the conceptual and analytical development, data processing, and the presence of uncertainty in the every phase. To consider nonlinearity in the system identification process, a method known as Generalized Iterative Least Squares-Extended Kalman Filter-Unknown Input (GLIS-EKF-UI), was developed earlier. Since it failed to identify structures in some cases, the authors recently proposed a new method denoted as Unscented Kalman Filter—Unknown Input- Weighted Global Iterations (UKF-UI-WGI). With the help of informative examples, the superiority of UKF-UI-WGI over GLIS-EKF-UI is documented in this paper. Since at the beginning of an inspection, the defects and their severity are expected to be unknown, the authors recommend UKF-UI-WGI for the rapid assessment of health of infrastructures.
KW - Kalman filters
KW - Nonlinear system identification
KW - Structural health assessment
KW - Uncertain measured information
KW - Unknown input excitation
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U2 - 10.1007/978-3-319-23597-4_13
DO - 10.1007/978-3-319-23597-4_13
M3 - Article
AN - SCOPUS:85043791006
SN - 2195-4356
SP - 165
EP - 186
JO - Lecture Notes in Mechanical Engineering
JF - Lecture Notes in Mechanical Engineering
ER -