Identification of abrupt stiffness changes of structures with tuned mass dampers under sudden events. Issue 6 (24th February 2020)

• Record Type: Journal Article
• Authors: Schleiter, Simon; Altay, Okyay
• Is Part Of: Structural control and health monitoring. Volume 27:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-24
• Subjects: abrupt stiffness changes -- adaptive unscented Kalman filter -- Kalman filter -- stiffness identification -- system identification -- tuned mass dampers; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2530
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: This paper presents a recursive system identification method for MDoF structures with tuned mass dampers (TMDs) considering abrupt stiffness changes in case of sudden events, such as earthquakes. Due to supplementary nonclassical damping of the TMDs, the system identification of MDoF + TMD systems disposes a challenge, in particular, in case of sudden events. These identification methods may be helpful for structural health monitoring of MDoF structures controlled by TMDs. A new adaptation formulation of the unscented Kalman filter allows the identification method to track abrupt stiffness changes. The paper, firstly, describes the theoretical background of the proposed system identification method and afterwards presents three parametric studies regarding the performance of the method. The first study shows the augmented state identification by the presented system identification method applied on a MDoF + TMD system. In this study, the abrupt stiffness changes of the system are successfully detected and localized under earthquake, impulse, and white noise excitations. The second study investigates the effects of the state covariance and its relevance for the system identification of MDoF + TMD systems. The results of this study show the necessity of an adaptive definition of the state covariance as applied in the proposed method. The third study investigates the effects of modeling on the performance of the identification method. Mathematical models with discretization of different orders of convergence and system noise levels are studied. The results show that, in particular, MDoF + TMD systems require higher order mathematical models for an accurate identification of abrupt changes.