## 1 Introduction  
It has become a trend to put equal emphasis on bridge's attractiveness and practicability. The concrete self-anchored suspension bridge is becoming competitive among the numerous bridge types, because of its economy, elegant appearance, and reasonable force transmission form [1; 2; 3]. Since most of the self-anchored suspension bridges are built in the thoroughfare of city transportation, the growth of traffic volume prompts the girder width to increase to 50 m or even more to meet the increasing lane requirements. The reliability of bridge is related to the lives of thousands of people and significant social impacts. The inevitable structural damages that are caused by the human factors, vehicle loads, and time-dependent effects will affect the bearing capacity and the durability of bridge [4]. The structural health monitoring (SHM) and safety evaluation are very necessary during the bridge service period. The research methods to grasp the bridge's mechanical properties mainly include the numerical simulation [5], lab experiment using reduced scale model [6], and field measurement [7]. The numerical simulation is difficult to obtain the stress states of structure details exactly. The lab experiment is limited by lab condition and model's fine level and it is difficult to reflect the actual bridge state. The way of integrating the numeric simulation predictions and field measured data is more appropriate. [PERSON] [8] presented a prediction model using the artificial neural network to assess the performance of Yonjung high-speed bridge. A short-term health monitoring system is used to collect the bridge behavior with different high-speed train's speeds. The statistical analysis is adopted to evaluate the girder displacement under speeds 165 to 403 Km/h. [PERSON] [9] investigated the prediction efficiency of two nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. The behavior of an experimentally scaled three-story building incorporated with a tuned mass damper (TMD) subjected to seismic loads was studied. The development of SHM tools is showing a trend of diversification. The wireless smart sensor network has been used widely because of its high flexibility and robust data management ability [10; 11; 12]. [PERSON] [13; 14; 15] presented a smart self-powered wireless sensor system and a data interpretation algorithm to detect the structural damages. [PERSON] [16] compared three common nonlinear methods that are used in the adjustment of Global Positioning System (GPS) positioning to improve the quality of GPS time series observations. [PERSON] [17] presented a novel SHM system based on the global navigation satellite system receivers to determine the modal properties of the Neckartal Bridge. [PERSON] [18] utilized the piezoceramic-based smart aggregates to perform the SHM for a reinforced concrete bridge column subjected to pseudo-dynamic loading. [PERSON] [19] developed a novel device consist of a bilaterally constrained beam and a piezoelectric polyvinylidene fluoride transducer to measure the thermal response of prestressed concrete bridge girders. [PERSON] [20] presented a fiber Bragg grating (FBG) vibration sensor for online monitoring of the cable vibration characteristics of Tongwamen bridge. [PERSON] [21] developed a new low-cost and accurate binary sensor comprised of wire bonded onto the girder using an adhesive to detect cracks in steel girders. A connected pipe system was used in the vertical deflection monitoring for a suspension bridge in China [22]. The fiber optic sensor [23; 24], acoustic emission technique [25], and ultrasonic monitoring technique [26] are also important tools in SHM field. The structural deformations and vibration properties of the already completed bridges can be monitored utilizing the nondestructive measurement tools.