Parameter Identification Of Shear-type Frame Structures Based On Kalman Filter

Civil engineering structures in operation usually suffer damage when they are exposed to external environment,working load and extreme load.The structural damage in turn causes changes of structural properties,which makes structural system identification(SSI)be a significant task of structural health monitoring.In traditional SSI algorithms,external excitations are assumed to be known in advance,however,it is too difficult to install so many sensors to measure all excitations/inputs and responses/outputs from real engineering structures.Therefore,it is urgent to propose some new SSI methods to deal with structures with limited inputs and outputs.As such,we conduct a research with a focus on modal parameter and physical parameter identification.First,with an aim of processing response signals with closely spaced mode components in civil engineering structures,a combined modal parameter identification method under unknown excitation is proposed;Second,a new method is proposed to deal with the problem of insufficient excitations/inputs and responses/outputs in civil engineering structures.The accuracy and effectiveness of the proposed methods is examined by numerical examples and a structural dynamic test.The main research work and innovations of this dissertion are expressed as follows:1.Based on the vibration responses with unknown excitations,a new method was proposed for the modal parameter identification of civil engineering structures,which is exactly a combination of random decrement technique,analytical mode decomposition,Hilbert transform,and Kalman filter.In this method,random decrement technique is first used to estimate the free vibration response from the measured vibration signals.Then,the analytical mode decomposition is employed to decompose the free vibration response into individual mode components.After that,Hilbert transform is used to estimate the natural frequency and damping ratio of each mode and finally Kalman filter is introduced to smooth the modal identification results.The accuracy and effectiveness of the proposed method is validated via a synthetic signal with closely-spaced components and a dynamic test of four-story steel frame structure under an unknown excitation.2.A new physical parameter identification method 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,which is suitable for non-Gaussian noise model,is introduced to reduce the linearization error caused by EKF.Meanwhile,LS estimation 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 shear type 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.