Identification of abrupt stiffness changes of structures with tuned mass dampers under sudden events

TL;DR

This paper introduces an advanced recursive system identification method using an adapted unscented Kalman filter to detect and localize abrupt stiffness changes in structures with tuned mass dampers during sudden events like earthquakes.

Contribution

It proposes a novel adaptation of the unscented Kalman filter for tracking abrupt stiffness changes in MDoF+TMD systems, enhancing structural health monitoring capabilities.

Findings

Successfully detects and localizes stiffness changes under various excitations.

Highlights importance of adaptive covariance in system identification.

Shows higher-order models improve accuracy in abrupt change detection.

Abstract

This paper presents a recursive system identification method for multi-degree-of-freedom (MDoF) structures with tuned mass dampers (TMDs) considering abrupt stiffness changes in case of sudden events, such as earthquakes. Due to supplementary non-classical damping of the TMDs, the system identification of MDoF+TMD systems disposes a challenge, in particular, in case of sudden events. This 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.