Structural health assessment at a local level using minimum information. (1st April 2015)

• Record Type: Journal Article
• Authors: Al-Hussein, Abdullah; Haldar, Achintya
• Is Part Of: Engineering structures. Volume 88(2015:Apr. 01)
• Page Start: 100
• Page End: 110
• Publication Date: 2015-04-01
• Subjects: Unscented Kalman filter -- Structural health assessment -- Nonlinear system identification -- Damage detection -- Unknown excitation -- Substructure -- Extended Kalman filter; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2015.01.026
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel structural health assessment method is proposed. It can assess health of large structural systems with minimum response information. It uses the Kalman filter-based concept integrated with substructure concept. It can identify location and severity of defects at the local finite element level. It is robust and superior to other methods even when structural behavior is nonlinear. 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.