## 1 Introduction  
Structural health monitoring (SHM) aims at observation and interpretation of full-scale and long-term performance of civil infrastructures. As a subset of SHM, vibration-based monitoring focuses on the dynamic responses of structures [1]. Although vibration data have a broad application in diagnosis and prognosis of the structural condition, identified modal characteristics are of specific interest because they are reflective of structural mass, stiffness, and even damping mechanism, which are representative of the structural condition [2, 3]. Therefore, in the past decades, vibration-based monitoring has rapidly grown into important academic research and the industrial application field. Among the development and applications of vibration-based monitoring, vibration-based damage assessment is of extreme importance due to the increasing costs of the maintenance of existing infrastructures and the catastrophic results of the structural failures [4, 5, 6]. A relatively large portion of vibration-based damage assessment methods is implemented by using a numerical model. By correlating the numerical model to the identified modal data of the real structures under the undamaged and damaged states, damage could be identified by the relative changes of the model parameters, such as the elemental or substructural stiffness [7]. However, for model-based damage assessment, real-world applications are sometimes prone to failure due to the inaccuracy of the numerical models, the measurement errors, and the associated numerical challenges to be addressed in solving the inverse problems [8; 9; 10]. The importance of the aforementioned problems has been gradually noticed and their solutions have been recently proposed both theoretically and practically [11; 12; 13; 14; 15]. First, the use of an accurate and highly reliable reference model has been emphasized by some researchers [16; 17; 18; 19; 20]. Errors of a Finite Element model can generally be classified into idealization error, discretization error, and parametric error. Although the last two kinds of errors can be significantly reduced by means of refined modelling and parametric model updating, the idealization errors can only be removed if appropriate modelling assumptions are introduced [21; 22; 23]. Especially, some dynamic characteristics of the bridges that are usually neglected during the design and construction stages should be investigated thoroughly, since the higher modes are typically more sensitive to the structural damage. Secondly, testing procedure, identification methods, and instrumentation require further improvement. Operational modal analysis (OMA) has arisen to be a viable technique to identify the modal parameters of structures under ambient conditions, i.e., wind and traffics, etc. [24; 25]. Since no artificial excitation, such as drop weight or shakers are needed, the method is relatively more competitive and efficient compared to the traditional experimental technique. Moreover, bridges can still be in operation, as traffic needs not to be blocked during the tests. Nevertheless, the response signals of ambient vibrations are typically of a very small amplitude, which are easily polluted by measurement noises. Not only some advanced identification methods are required [26], but also the versatile testing technique and highly sensitive and reliable acquisition systems are important. In this regard, the combination of accelerometer and strain senor can contribute to derive very accurate modal curvature of bridges for damage assessment [27; 28]. Moreover, the use of the novel wireless sensing system is advantageous in comparison to traditional cabled system for its quick deployment, easy configuration, and little interference, which helps to improve the spatial resolution of the experimental mode shapes [29; 30]. Under a research project financed by the Spanish Ministry of Science and Innovation, a high-synchronous wireless sensing system has been developed for OMA [31].