Brige Damage Identification Based On Vehicle-bridge Coupling Vibration

The bridge structures suffer damages with different levels caused by varied natural hazards and environmental affect. The important and challenging task in maintaining the structural health is how to detect damage and monitor the damage growth. However, for most of the structure damage identification methods, it is difficult to be applied in actual project. Therefore, more in-depth study of bridge structure damage identification method is needed to be accomplished. In the recent years, many scientist use the vibration signal directly to identify structural damage, because the vibration signal can fully retain the authenticity of injury information. Extended Kalman filter (EKF) is one of the most widely-used time-domain methods for nonlinear system identification because of good performance on identification accuracy and arithmetic robustness. The application of the EKF to engineering structures is tried in spite of noise-contaminated response measurementsThe identification of structure local damage in EKF algorithm is a typical dynamic inverse problem. For more identified parameters, the accuracy of damage identification will decrease significantly due to ill-condition and computation convergence problems. The ill-condition of inverse problem may cause the strong singularity of gain matrix which will exaggerate the noise interruption, and then bring about non-positive state covariance matrix and divergent algorithm. In order to solve this problem, the EKF algorithm with l1-norm regularization is proposed in this paper. A pseudo-measurement technique is utilized to enforce a l1-norm constraint in the method. In this paper, free vibration signal is used as the observation, the experiment and numerical calculation for simply supported and cantilever beam are carried out to validate the performances of the proposed algorithm. Application in a plane frame demonstrates the effect of the proposed method for complex structure.Compared to the free vibration signal, the bridge vibration signal excited by vehicle, can be obtained without interrupting traffic, which are generally more readily available. However, most of the damage identification methods based on coupled vibration signal are time-consuming, making it difficult to detect damage in time. Because the extended Kalman filter algorithm is a recursive algorithm, with the good performance of a small amount of calculation. So we further apply extended Kalman filter method to detect damage using the vehicle-bridge coupling vibration signal as the observation. Firstly, a simple sprung mass model is applied to simulate the vehicle, the effect of different speed, weight, bridge on the identification result are analyzed. Then, the vehicle is modeled by 1/4 vehicle model and half car model, and damage identification results are discussed. The results show that the extended Kalman filtering method based on the coupled vibration signal, can identify the damage accurately, and can also well suppress interference noise.