KEYWORDS: Filtering (signal processing), Earthquakes, Structural health monitoring, Complex systems, System identification, Reliability, Damage detection, Chemical elements, Error analysis, Signal to noise ratio
An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. Innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently. The problem is quite challenging, in particular when the structure is nonlinear. In this paper, we proposed a new data analysis method, referred to as the sequential nonlinear least square estimation (SNLSE), for the on-line identification of nonlinear structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify on-line the time-varying system parameters of nonlinear structures. The accuracy and effectiveness of the proposed approach has been demonstrated using a nonlinear elastic structure and nonlinear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.
An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection based on measured vibration data have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes. The proposed technique is capable of tracking the abrupt change of system parameters from which the event and severity of structural damages can be detected. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noises. The proposed technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of linear and nonlinear hysteretic structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting the structural damages using vibration data from the health monitoring system.
In this paper, we present two control strategies for applications to civil engineering structures, referred to as the generalized H2 control and L1 control, respectively. Both control strategies are capable of addressing the performance-based design of structures, in the sense that the design requirements for the peak response quantities, such as peak interstory drifts, peak shear forces, peak floor accelerations, etc., can be satisfied. Likewise, these two controllers minimize the upper bound of the peak response of the controlled output vector. The design procedures for these two controllers are formulated in the framework of linear matrix inequalities (LMIs) so that the LMI toolbox in MATLAB can be used effectively and conveniently for the controller design. These control strategies are applied herein to the wind-excited benchmark problem to demonstrate their applicability to practical problems as well as their control performances. Simulation results illustrate that the performances of both the generalized H2 controller and the L1 controller are very plausible in comparison with the LQG control method.
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