Abstract
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.
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References
Al-Hussein A, Haldar A (2015) A comparison of unscented and extended Kalman filtering for nonlinear system identification. In: 12th international conference on applications of statistics and probability in civil engineering (ICASP12-2015). Vancouver, Canada
Al-Hussein A, Haldar A (2015) Novel unscented Kalman filter for health assessment of structural systems with unknown input. J Eng Mech ASCE 141(7):04015012-1–04015012-13. doi:10.1061/(ASCE)EM.1943-7889.0000926
Al-Hussein A, Haldar A (2015) Structural health assessment at a local level using minimum information. Eng Struct 88:100–110. doi:10.1016/j.engstruct.2015.01.026
Al-Hussein A, Haldar A (2015) Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems. Structural Control and Health Monitoring. doi:10.1002/stc.1764
ANSYS version 15.0 (2013).The Engineering Solutions Company
Clough RW, Penzien J (2003) Dynamics of structures, 3rd edn. Computers and Structures, California
Cross EJ, Worden K, Farrar CR (2013) Chapter 1—structural health assessment for civil infrastructure. Health Assessment of Engineered Structures: Bridges, Buildings and Other Infrastructures, World Scientific Publishing Co., Editor, pp 1–31
Das AK (2012) Health assessment of three dimensional large structural systems using limited uncertain dynamic response information. PhD dissertation, Department of Civil Engineering and Engineering Mechanics, University of Arizona, Tucson, Arizona
Haldar A (ed) (2013) Health assessment of engineered structures: bridges, buildings and other infrastructures. World Scientific Publishing Co., New Jersey
Haldar A, Das AK, Al-Hussein A (2013) Data analysis challenges in structural health assessment using measured dynamic responses. Adv Adapt Data Anal 5(4):1350017-1–1350017-22
Hoshiya M, Saito E (1984) Structural identification by extended Kalman filter. J Eng Mech ASCE 110(12):1757–1772
Jazwinski AH (1970) Stochastic process and filtering theory. Academic Press, Inc., New York
Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. In: Proceedings of the IEEE, vol 92, no 3, pp 401–422
Julier SJ, Uhlmann JK, Durrant-Whyte HF (1995) A new approach for filtering nonlinear systems. In: Proceedings of the american control conference, vol 3. Seattle, Washington, pp 1628–1632
Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82:35–45
Kalman RE, Bucy RS (1961) New results in linear filtering and prediction theory. J Fluids Eng 83(1):95–108
Katkhuda H, Haldar A (2008) A novel health assessment technique with minimum information. Struct Control Health Monit 15(6):821–838
Katkhuda H, Martinez-Flores R, Haldar A (2005) Health assessment at local level with unknown input excitation. J Struct Eng ASCE 131(6):956–965
Ling X, Haldar A (2004) Element level system identification with unknown input with Rayleigh damping. J Eng Mech ASCE 130(8):877–885
Martinez-Flores R (2005) Damage assessment potential of a novel system identification technique—experimental verification. PhD dissertation, Department of Civil Engineering and Engineering Mechanics, University of Arizona, Tucson, Arizona
Maybeck PS (1979) Stochastic models, estimation, and control theory. Academic Press Inc, UK
Vo PH, Haldar A (2003) Post-processing of linear accelerometer data in structural identification. J Struct Eng 30(2):123–130
Vo PH, Haldar A (2008) Health assessment of beams—experimental verification. Struct Infrastruct Eng 4(1):45–56
Wang D, Haldar A (1994) Element-level system identification with unknown input information. J Eng Mech ASCE 120(1):159–176
Wang D, Haldar A (1997) System identification with limited observations and without input. J Eng Mech ASCE 123(5):504–511
Welch G, Bishop G (1995) An introduction to the Kalman filter. Technical report TR95-041, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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.
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Haldar, A., Al-Hussein, A. (2016). Prognostics and Structural Health Assessment Using Uncertain Measured Response Information. In: Kumar, U., Ahmadi, A., Verma, A., Varde, P. (eds) Current Trends in Reliability, Availability, Maintainability and Safety. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-23597-4_13
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