Pounding tuned mass damper (PTMD) is a novel type of passive damper. The PTMD utilizes collisions or impacts of a tuned mass with viscoelastic materials to efficiently dissipate the vibration energy of primary structures. The previous studies have verified its effective damping performance on a full-scale subsea jumper and other structures in air. This paper presents the first-ever experimental verification of a submerged PTMD system for vibration control of pipelike structures underwater. To facilitate the experimental studies, a vertical vibration system consisting of 4 springs and a cylindrical steel pipe was designed and set up in a water tank. Furthermore, a numerical method considering the effect of the added mass is described to estimate the natural frequencies of a submerged cylindrical pipe. Therefore, experimental results demonstrate that the PTMD system is effective and efficient to suppress the forced vibrations of the submerged cylindrical pipe at the tuned frequency and is also robust over a range of detuning frequencies.

Damage identification is an indispensable part for successive applications of structural health monitoring. In practical applications, however, time-varying environmental and operational conditions, such as temperature and external loadings, often overwhelm the subtle structural changes caused by damage. It is therefore of great significance to remove those structural changes (damage features) caused by external influences from actual structural damage. In this paper, a new damage identification method based on Kalman filter and cointegration (KFC) is developed, and the environmental effects on damage indicator are removed by the cointegration process of the Kalman filtered coefficients. The cointegration relationship between structural frequencies is first established with augmented Dickey-Fuller test and Johansen procedure. The cointegration coefficients are then used to constitute the Kalman filter (KF) state vector, and the recursive KF process is intrigued to on-line estimate the change of structure states. To enhance the importance of incoming new observations in the KF, we introduce an adaptive fading factor into the conventional KF. Numerical simulation of a truss bridge is used to validate the effectiveness of the proposed KFC method for damage identification under varying temperature, even with 10% noise. Finally, the KFC method is applied to a cable-stayed bridge built in China (Tianjin Yonghe Bridge), and two structural damage scenarios are successfully identified. The advantages of the proposed KFC method are its ability to eliminate ambient temperature influences and identify structural damage on-line.

Summary The conventional extended Kalman filter (EKF) for detecting structural damage with measured responses is a dynamic inverse problem. With the increase in size of the extended state vector, EKF suffers from low accuracy and convergence difficulty due to the ill‐condition of the inverse problem and increased computational error. To overcome the aforementioned drawbacks, an improved EKF method based on lp regularization (EKF‐lp) is proposed in this paper. In the proposed method, the sparse characteristic on the distribution of local damage, as a priori information, is introduced into EKF by the lp regularization technique. Then, the unconstrained optimization problem of EKF becomes the optimization problem with the lp‐norm constraint. To obtain a recursive solution of the constrained optimization, a pseudo‐measurement equation is used to embed the lp‐norm constraint into the recursive EKF steps. In addition, a precise integration method is employed in the time update step to improve the accuracy of state prediction. To select an appropriate p value in the EKF‐lp method, a novel L‐surface approach is proposed. Finally, the proposed EKF‐lp method is compared with the existing EKF with Tikhonov regularization method and EKF with l1 regularization method on a beam example and an experiment of a three‐story shear building. It is shown that the proposed method is stable and reliable, and its identification precision is higher than the other methods. Moreover, it requires significant less measurements than conventional methods.