A new physical parameter identification method for shear frame structures under limited inputs and outputs

Abstract

A new physical parameter identification method with a name of EKPF-LS is proposed for shear frame structures under limited inputs and outputs by a combination of extended Kalman particle filter (EKPF) and least square (LS) algorithm. The basic principle of EKPF-LS is to establish the proposed distribution function of the particle filter through EKF-LS. In this method, EKPF is introduced to get rid of the restriction of Gaussian white noise model and reduce the linearization error caused by EKF. Meanwhile, LS is utilized to address the problem of unmeasured excitation estimations. The effectiveness and accuracy of the proposed EKPF-LS method is verified by a numerical example of a four-story hysteretic shear frame under an earthquake excitation and an experimental test of a four-story shear type frame using Gaussian white noise and sine sweep signal as excitations, respectively. Gaussian colored noises are then added to the solved and measured response signals in the numerical example and experimental test, respectively. The results demonstrate that the proposed method can identify the stiffness of shear frame structures effectively and is superior to the existed EKF-LS approach when the structural system is nonlinear structural system or Colored noise model.

A generalized extended Kalman particle filter with unknown input for nonlinear system‐input identification under non‐Gaussian measurement noises

“…However, for an inverse problem of structural identification conditional on measurement responses, the PDFs of measurement noises and system noises are unknown. Thus, it is generally impossible to directly use Equation (18) for the calculation of importance weights 27 . introduces a particle swarm optimization (PSO) fitness function developed by 42 to calculate the importance weights.…”

“…Thus, it is generally impossible to directly use Equation (18) for the calculation of importance weights. 27 introduces a particle swarm optimization (PSO) fitness function developed by 42 to calculate the importance weights. The PSO fitness function is similar to normal distribution.…”

“…Later on, Sen et al 26 developed improved interacting particle‐Kalman filter for the simultaneous identification of structural damage and unknown earthquake excitation signal. Liu et al 27 presented an extended Kalman particle filter with least‐square algorithm (EKPF‐LS) to identify structural parameters along with unknown inputs. However, all the above researches require that structural observation equations must contain the unknown excitations, that is, with direct feedthrough of unknown inputs 28 .…”

A Smoothing EKF-UI-WDF Method for Simultaneous Identification of Structural Systems and Unknown Seismic Inputs without Direct Feedthrough

“…Recursive Filter. Te proposed method aims to simultaneously identify the unknown seismic input and structural system based on the derived state equation ( 4) and the observation equation (10).…”

“…Moreover, due to the infuence of transmission media, even the recorded data may have large errors [5]. Terefore, in the feld of structural health monitoring, unknown inputs and structural states are usually identifed through observed vibration response data of structures [6][7][8][9][10]. However, simultaneous identifcation of unknown seismic excitations and corresponding structural system states using partial responses of structures is still a challenging problem, especially when the structural parameters are unknown.…”