A Novel Technique for Structural Health Assessment in the Presence of Nonlinearity

Persistent Link:
http://hdl.handle.net/10150/578896
Title:
A Novel Technique for Structural Health Assessment in the Presence of Nonlinearity
Author:
Al-Hussein, Abdullah Abdulamir
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Release 01-Jan-2016
Abstract:
A novel structural health assessment (SHA) technique is proposed. It is a finite element-based time domain nonlinear system identification technique. The procedure is developed in two stages to incorporate several desirable features and increase its implementation potential. First, a weighted global iteration with an objective function is introduced in the unscented Kalman filter (UKF) procedure in order to obtain stable, convergent, and optimal solution. Furthermore, it also improves the capability of the UKF procedure to identify a large structural system using only a short duration of responses measured at a limited number of dynamic degrees of freedom (DDOFs). The combined procedure is denoted as unscented Kalman filter with weighted global iteration (UKF-WGI). Then, UKF-WGI is integrated with iterative least-squares with unknown input (ILS-UI) in order to increase its implementation potential. The substructure concept is also incorporated in the procedure. The integrated procedure is denoted as unscented Kalman filter with unknown input and weighted global iteration (UKF-UI-WGI). The two most important features of the method are that it does not need information on input excitation and uses only limited number of noise-contaminated response information to identify structural systems. Also, the method is able to identify the defects at the local element level by tracking the changes in the stiffness of the structural elements in the finite element representation. The UKF-UI-WGI procedure is implemented in two stages. In Stage 1, based on the location of input excitation, the substructure is selected. Using only responses at all DDOFs in the substructure, ILS-UI can identify the input excitation time-histories, stiffness parameters of all the elements in the substructure, and two Rayleigh damping coefficients. The outcomes of the first stage are necessary to initiate UKF-WGI. Using the information from Stage 1, the stiffness parameters of all the elements in the structure are identified using UKF-WGI in Stage 2. To demonstrate the effectiveness of the procedure, health assessment of relatively large structural systems is presented. 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 also 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 accurately. The comparative studies are also conducted between the proposed methods and available methods in the literature. First, it is between the UKF-WGI and extended Kalman filter with weighted global iteration (EKF-WGI) procedure. Then, it is between UKF-UI-WGI and generalized iterative least-squares extended Kalman filter with unknown input (GILS-EKF-UI) procedure, developed earlier by the research team. It is demonstrated that the proposed UKF-based procedures are superior to the EKF-based procedures for SHA.
Type:
text; Electronic Dissertation
Keywords:
Extended Kalman filter; Nonlinear system identification; Structural health assessment; Unknown input; Unscented Kalman filter; Civil Engineering; Damage detection
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Civil Engineering
Degree Grantor:
University of Arizona
Advisor:
Haldar, Achintya

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleA Novel Technique for Structural Health Assessment in the Presence of Nonlinearityen_US
dc.creatorAl-Hussein, Abdullah Abdulamiren
dc.contributor.authorAl-Hussein, Abdullah Abdulamiren
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.releaseRelease 01-Jan-2016en
dc.description.abstractA novel structural health assessment (SHA) technique is proposed. It is a finite element-based time domain nonlinear system identification technique. The procedure is developed in two stages to incorporate several desirable features and increase its implementation potential. First, a weighted global iteration with an objective function is introduced in the unscented Kalman filter (UKF) procedure in order to obtain stable, convergent, and optimal solution. Furthermore, it also improves the capability of the UKF procedure to identify a large structural system using only a short duration of responses measured at a limited number of dynamic degrees of freedom (DDOFs). The combined procedure is denoted as unscented Kalman filter with weighted global iteration (UKF-WGI). Then, UKF-WGI is integrated with iterative least-squares with unknown input (ILS-UI) in order to increase its implementation potential. The substructure concept is also incorporated in the procedure. The integrated procedure is denoted as unscented Kalman filter with unknown input and weighted global iteration (UKF-UI-WGI). The two most important features of the method are that it does not need information on input excitation and uses only limited number of noise-contaminated response information to identify structural systems. Also, the method is able to identify the defects at the local element level by tracking the changes in the stiffness of the structural elements in the finite element representation. The UKF-UI-WGI procedure is implemented in two stages. In Stage 1, based on the location of input excitation, the substructure is selected. Using only responses at all DDOFs in the substructure, ILS-UI can identify the input excitation time-histories, stiffness parameters of all the elements in the substructure, and two Rayleigh damping coefficients. The outcomes of the first stage are necessary to initiate UKF-WGI. Using the information from Stage 1, the stiffness parameters of all the elements in the structure are identified using UKF-WGI in Stage 2. To demonstrate the effectiveness of the procedure, health assessment of relatively large structural systems is presented. 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 also 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 accurately. The comparative studies are also conducted between the proposed methods and available methods in the literature. First, it is between the UKF-WGI and extended Kalman filter with weighted global iteration (EKF-WGI) procedure. Then, it is between UKF-UI-WGI and generalized iterative least-squares extended Kalman filter with unknown input (GILS-EKF-UI) procedure, developed earlier by the research team. It is demonstrated that the proposed UKF-based procedures are superior to the EKF-based procedures for SHA.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectExtended Kalman filteren
dc.subjectNonlinear system identificationen
dc.subjectStructural health assessmenten
dc.subjectUnknown inputen
dc.subjectUnscented Kalman filteren
dc.subjectCivil Engineeringen
dc.subjectDamage detectionen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorHaldar, Achintyaen
dc.contributor.committeememberHaldar, Achintyaen
dc.contributor.committeememberDesai, Chandrakanten
dc.contributor.committeememberKundu, Tribikramen
dc.contributor.committeememberZhang, Lianyangen
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