No abstract

Periodic health assessment of large civil engineering structures is an effective way to ensure safe performance all through their service lives. Dynamic response-based structural health assessment can only be performed under normal/ambient operating conditions. Existing Kalman filter-based parameter identification algorithms that consider parameters as the only states require the measurements to be sufficiently clean in order to achieve precise estimation. On the other hand, appending parameters in an extended state vector in order to jointly estimate states and parameters is reported to have convergence issues. In this article, a constrained version of the dual extended Kalman filtering (cDEKF) technique is employed in which two concurrent extended Kalman filters simultaneously filter the measurement response (as states) and estimate the elements of state transition matrix (as parameters). Constraints are placed on stiffness and damping parameters during the estimation of the gain matrix to ensure they remain within realistic bounds. The proposed method is compared against the existing Kalman filter-based parameter identification techniques on a three-degrees-of-freedom mass-spring-damper system adopting both unconstrained and constrained estimation approaches. cDEKF is then employed on a numerical six-story shear frame and a 3D space truss to validate its robustness and efficacy in identifying structural damage. The results suggest that cDEKF algorithm is an efficient online damage identification scheme that makes use of ambient vibration response. KEYWORDSdual extended Kalman filter, constrained Kalman filter, online damage detection, structural health monitorin 1 Struct Control Health Monit. 2017;24:e1961. wileyonlinelibrary.com/journal/stc

No abstract

Standard filtering techniques for structural parameter estimation assume that the input force is either known or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, force must therefore also be estimated. In this paper, the input force is considered to be an additional state that is estimated in parallel to the structural parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, an interacting Particle-Kalman filter is used to target systems with correlated noise. Alongside this, a second filter is used to estimate the seismic force acting on the structure. In the proposed algorithm, the parameters and the inputs are estimated as being conditional on each other, thus ensuring stability in the estimation. The proposed algorithm is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system and a five-story building structure.The stability of the proposed filter is also tested by subjecting it to a sufficiently long measurement time history. The estimation results confirm the applicability of the proposed algorithm.

Standard filtering techniques for structural parameter estimation assume that the input force either is known exactly or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, a novel algorithm is proposed to estimate the force as additional state in parallel to the system parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, interacting Particle-Kalman filter [1] is employed targeting systems with correlated noise. Alongside a second filter is employed to estimate the seismic force acting on the structure. The proposal is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system. The estimation results confirm the applicability of the proposed algorithm.