Abdullah Al-Hussein

Novel Unscented Kalman Filter for Health Assessment of Structural Systems with Unknown Input

AbstractA novel procedure for structural health assessment, denoted as unscented Kalman filter with unknown input (UKF-UI), is proposed using the nonlinear system identification concept. To increase its implementation potential, a substructure concept is introduced, producing a two-stage approach. It integrates the unscented Kalman filter concept and an iterative least-squares technique. The two most important features of the method are that it does not need the information on the time history of the excitation to identify structural systems represented by finite elements, and that it can identify defects in them using only a limited amount of noise-contaminated nonlinear response information. The proposed method is robust enough to detect the locations and severity of defects at different locations in the structure. The defect detection capability increases significantly if the defective member is in the substructure or close to it. The method is conclusively verified with the help of two examples using ...

DATA ANALYSIS CHALLENGES IN STRUCTURAL HEALTH ASSESSMENT USING MEASURED DYNAMIC RESPONSES

The authors and their research team developed several nondestructive inspection-based structural health assessment and monitoring procedures to detect defect at the local element level, representing structures by finite elements. They are based on time domain nonlinear system identification-based concept. By tracking changes in the identified system parameters of the elements, the location and severity of defect can be assessed. To increase their implementation potential, only limited numbers of very short durations measured noise-contaminated acceleration time-histories are used in the identification algorithms. Numerous multi-disciplinary advanced data processing schemes used in developing these algorithms are briefly discussed. Presence of uncertainties in data processing and mitigation strategies are emphasized.

Structural health assessment using extended and unscented Kalman filters

Structural health assessment procedures using extended and unscented Kalman filter concepts are presented and compared. The extended Kalman filter (EKF)-based algorithm proposed earlier for nonlinear system identification comes with limitations. The linearisation process used in EKF may lead to non-convergence for higher level of nonlinearity. To address the deficiency, the authors proposed a new algorithm known as unscented Kalman filter with unknown input and weighted global iteration (UKF-UI-WGI). In this study, a weighted global iteration technique with objective function is incorporated with the UKF algorithm in order to improve its efficiency. To generate the information required to implement the algorithm, it is integrated with least-squares-based algorithm. The stability, convergence, and robustness of the UKF-UI-WGI over EKF-based algorithm are compared in terms of several parameters including the sampling interval, duration of responses, and the dimension of the frames. With the help of examples, the overall superiority of UKF-UI-WGI over EKF-based algorithm is established.

A new extension of unscented Kalman filter for structural health assessment with unknown input

A time-domain nonlinear system identification (SI)-based structural health assessment (SHA) procedure, using Unscented Kalman Filter (UKF) concept, is presented in this paper. It is a two-stage procedure. It integrates an iterative least squares technique and the unscented Kalman filter concept. The authors believe that the integrated procedure significantly improves the basic UKF concept. The procedure can assess the health of a structure using only a limited number of noise-contaminated acceleration time-histories measured only at a small part of a structure and does not need information on input excitation. The structures are represented by finite element models and the location and severity of defect(s) are assessed by tracking the changes in the stiffness properties of individual elements from their expected values. With the help of examples, it is demonstrated that the method is capable of accurately identifying defect-free and defective states of structures. 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. It is demonstrated that the accuracy of the method is much better than the other methods currently available for the structural health assessment. It is also superior to the extended Kalman filter. Considering the accuracy and robustness, the procedure can be used as a nondestructive structural health assessment procedure.

Structural damage prognosis of three-dimensional large structural systems

Abstract A novel procedure for the health assessment of large three-dimensional (3D) structures with several significant attractive features and improved implementation potential is proposed. Structures are represented by 3D finite elements and a substructure concept is used so that acceleration time histories can be measured only at small part(s) of the structure. Just by measuring relatively few noise-contaminated responses in the substructure, the health of the whole structure can be assessed by the system identification (SI) concept by tracking the stiffness parameter of all the elements using a significantly improved unscented Kalman filter (UKF) algorithm. Since measuring excitation time histories can be very problematic and expensive, the UKF algorithm is integrated with 3D iterative least-squares with unknown input algorithm. UKF fails to identify large structures due to convergence-related issues. The authors used short duration responses and multiple global iterations with weight factor and objective function instead of one long duration response generally used in UKF. For the preselected excitation, short duration eliminates multiple sources of excitation beyond the control of inspector. The weight factor helps accurately locate the defect spot. With informative examples, it is documented that the proposed method is superior to various other forms of Kalman filter-based algorithms.

A comparison of unscented and extended Kalman filtering for nonlinear system identification

A nonlinear system identification-based structural health assessment procedure is presented in this paper. The procedure uses the unscented Kalman filter (UKF) concept. The weighted global iteration with an objective function is incorporated with the UKF algorithm to obtain stable, convergent, and optimal solution. An iterative least squares technique is also integrated with the UKF algorithm. The procedure is capable of assessing health of any type of structures, represented by finite elements. It can identify the structure using limited noise-contaminated dynamic responses, measured at a small part of large structural systems and without using input excitation information. In order to demonstrate its effectiveness, the proposed procedure is compared with the extended Kalman filter (EKF)-based procedure. For numerical verification, a two-dimensional five-story two-bay steel frame is considered. Defect-free and two defective states with small and severe defects are considered. The study shows that the proposed UKF-based procedure can assess structural health more accurately and efficiently than the EKF-based procedures for nonlinear system identification.

Complexities in Assessing Structural Health of Civil Infrastructures

The complexity in the health assessment of civil infrastructures, as it evolves over a long period of time, is briefly discussed. A simple problem can become very complex based on the current needs, sophistication required, and the technological advancements. To meet the current needs of locating defect spots and their severity accurately and efficiently, infrastructures are represented by finite elements. To increase the implementation potential, the stiffness parameters of all the elements are identified and tracked using only few noise-contaminated dynamic responses measured at small part of the infrastructure. To extract the required information, Kalman filter concept is integrated with other numerical schemes. An unscented Kalman filter (UKF) concept is developed for highly nonlinear dynamic systems. It is denoted as 3D UKF-UI-WGI. The basic UKF concept is improved in several ways. Instead of using one long duration time-history in one global iteration, very short duration time-histories and multiple global iterations with weight factors are used to locate the defect spot more accurately and efficiently. The capabilities of the procedure are demonstrated with the help of two informative examples. The proposed procedure is much superior to the extended Kalman filter-based procedures developed by the team earlier.

Prognostics and Structural Health Assessment Using Uncertain Measured Response Information

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.

Computational Intelligence for Structural Identifications

Structural health assessment using the structural identification concept using measured dynamic response information in time domain was considered to be not possible in the late seventies. With the help of comprehensive analytical and laboratory investigations, the research team at the University of Arizona conclusively documented that the above statement is not correct. In fact, they showed that the concept has several advantages over other available methods. Considering the implementation potential, the concept appeared to be very desirable. The team proposed several intelligent schemes to address many challenges. They used mathematical concepts used in other disciplines, extensively modified them and proposed few novel concepts. Some of them are briefly presented in this paper and their novelties are described with the help of several informative examples. The concepts presented in the paper cross the disciplinary boundaries and showcase benefits of computational intelligence.

Algorithmic and computing technologies for health assessment of real structures in the presence of nonlinearity and uncertainty

The research team at the University of Arizona proposed several novel structural health assessment (SHA) algorithms. Structures are represented by finite elements (FE) and the health is assessed by identifying the stiffness parameters of all the elements and comparing them with expected values, or with previous values, or observing differences between similar elements. They can identify the location and severity of defect and exact location within a defective element. These algorithms use several system identification (SI) based concepts with different levels of sophistications. They do not require excitation information and can assess the health of large structural systems using only limited noise-contaminated acceleration timehistories measured at a small part of a structure. They are widely available in the literature. However, algorithmic and computational rigors of them are generally not presented in technical papers due to severe page limitation. Some of them are briefly presented in this paper without discussing the specific algorithms.