Parameter Identification And Rapid Damage Assessment Method Of Bridge Group Under Strong Earthquake Based On Monitoring Data

In recent years,earthquake disasters have occurred frequently in China.As the hub of the transportation network,the damage status of bridges plays an important role in post-disaster emergency rescue.It is unrealistic to install structure health monitoring systems on all bridges.The traditional rapid assessment based on vulnerability methods can only give the probability of different failure states of the structure.Based on this situation,this paper mainly studies regional bridge earthquake disaster monitoring and rapid post-earthquake quantitative assessment.The full text includes three research points,the establishment of regional bridge earthquake disaster monitoring system,the parameter identification of monitoring bridges and the inversion of ground motion input and condition assessment,and the fast nonlinear dynamic time history analysis and state assessment of unmonitored bridges.The specific research contents of this paper are as follows:(1)Ground motion input inversion based on the monitored absolute acceleration response.Under earthquake excitation,acceleration response is relatively easy to monitor,while velocity and displacement are relatively difficult to monitor.In this study,the transformation space method is used to integrate the absolute acceleration into absolute velocity and absolute displacement,which can eliminate the problems of unknown initial value and integral drift in the traditional integration method,and then the least square method is used to identify the structural parameters of above the first floor;By introducing modal information,the stiffness of the first floor is identified;Finally,assuming that the ground motion input at any three adjacent moments is equal to value at the middle moment,a simplified ground motion input inversion algorithm is proposed.Numerical simulation and shaking table test results show that the proposed method can invert different types of seismic waveforms well and has good robustness.For strong earthquakes scenes,the inversion results are in good agreement with the true values in the low-frequency region,which meets the input requirements for rapid post-earthquake assessment of regional structures.(2)Improvement of the Sigma point Kalman filter algorithm.This section mainly studies the parameter identification and state estimation of the monitored structure when the ground motion input is known.Traditional methods such as extended Kalman filter(EKF)and unscented Kalman filter(UKF)have problems such as weak ability to track time-varying parameters,and the matrix singularity when the covariance matrix is squared which leads to computational instability.Based on the square root unscented Kalman filter,a modified strong tracking square root unscented Kalman filter(MSTSRUKF)is proposed in this section.Firstly,QR decomposition is used to improve the square root calculation method of the covariance matrix in the square root unscented Kalman filter algorithm,so that the recursive process is unconditionally mathematically stable;Secondly,the calculation method of the square root of the covariance matrix in the filter update is improved,and the equivalent form of the observation matrix is introduced at the same time,so as to ensure the stability of the algorithm and avoid solving the Jacobian matrix of the complex system;Finally,the strong tracking filtering technology is introduced to update the time prediction covariance matrix,so that the algorithm has the ability to track time-varying parameters.The numerical simulation results show that the MSTSRUKF algorithm can effectively identify the abrupt parameters of linear and nonlinear systems,and can predict the structural state more accurately.The mathematics is stable in the recursive process,and the algorithm has strong anti-noise performance.The experimental results show that because the zero term with negative weight coefficient is discarded,there is a phenomenon of filter divergence for strongly nonlinear systems.Therefore,the central difference Kalman filter algorithm with all positive weight coefficients is improved.Numerical simulation and experimental results show that the modified strong tracking square root central difference Kalman filter(MSTSRCDKF)algorithm has stable mathematical recursive process and high filtering accuracy and robustness.(3)Parameter identification of polyline constitutive model.The polyline constitutive model has few control parameters and clear physical meaning,but its mathematical expression is complex and difficult to identify.Aiming at the parameter identification of polyline constitutive model,a modified square root constrained parameter cubature Kalman filter(MSRCP-CKF)algorithm is proposed.The proposed method takes the parameter to be identified as the state vector,which can reduce the dimension of the state vector and the time costs of calculation;Based on Sigma point Kalman filter to avoid solving Jacobian matrix,then realize polyline constitutive model parameter identification;A constrained parameter technique is introduced in the modified square root cubature Kalman filter to preserve the nonnegative physical meaning of the parameters to be identified.The modified Takeda model is employed to verify the effectiveness of the proposed method.Numerical simulations and shaking table tests of a two-story reinforced concrete frame structure show that the proposed algorithm can effectively identify the parameters of the modified Takeda model.At the same time,the structural response is not sensitive to the stiffness-yield ratio,therefore,the identification results are relatively poor.(4)The framework of regional bridge earthquake disaster monitoring system is constructed.The characteristics and needs of regional bridge earthquake disaster monitoring are studied,and a regional bridge monitoring system based on the Internet of Things is constructed.The system framework integrates structural response monitoring,ground motion input inversion,regional bridge dynamic time-history analysis and state assessment,and can quickly make post-earthquake assessments of regional structures without the data of the seismic monitoring system.In this system,for the monitored structure,the monitoring data are used to identify the structural parameters,estimate the structural response,and then evaluate the structural damage state;For unmonitored structures,a state evaluation method based on simplified model dynamic time history analysis is proposed.(5)Rapid post-earthquake assessment of regional bridges based on nonlinear dynamic time-history response analysis.According to the characteristics of girder bridges,a standardized simplified model of girder bridges is established,which can consider design parameters.Based on the ground motion cloud image obtained by the regional bridge monitoring system,nonlinear dynamic time-history response analysis can be performed quickly,and then the damage status of each bridge component and the overall damage state can be quickly evaluated according to the damage index.The proposed method considers the amplification characteristics of the site to the seismic wave,and can quantitatively evaluate the damage state,determine whether the beam falls and the cumulative damage to the structure caused by aftershocks.Taking 85 bridges in a certain area as an example,numerical simulation of E1 earthquake and E2 earthquake is carried out.The results show that the bearing connection mode has a great influence on the damage status of the bridge piers.The method proposed in this study can quantitatively evaluate the damage status of each bridge,and the evaluation results can provide favorable information for post-earthquake rescue.