## 1 Introduction  
The regular monitoring and assessment of a bridge's structural behaviour is essential for the early detection of construction defects. It enables the maintenance and repair of the system at an early stage, ensuring the safety and reliability of the structure at a minimal cost. Decades ago, visual inspection was the most common method of detecting structural damage. However, visual inspection solutions are often quite laborious and time-consuming, which consumes high costs for workers and assessors, with a low overall efficiency. This is especially true when structural dismantling (e.g., cutting, taking samples, among others) is required to access the area to be tested; these have the effect of changing the physical properties, and can thus reduce the structure's bearing capacity. In addition, visual inspection techniques can only help identify damage that is visible on the structure's surface. For large and complex structures (such as cable-stayed bridges and suspension bridges), the visual inspection method is challenging to use, and is therefore no longer suitable. In the past, when sensor technology had not yet been developed, structural health monitoring was often performed based on geodetic engineering and machinery [1; 2; 3; 4]. Structural health monitoring is based on the characteristics of geometrical factors, such as settlement, deformation, and displacement. In this regard, creating a coordinate system according to the country's coordinate grid is necessary. On the other hand, these coordinate systems should meet the technical requirements specified by the national standards. The observations can then be made at the predetermined locations, and compared with the first measurement. The results are then processed and evaluated by experts, in accordance with national specifications. However, this approach still retains many disadvantages; both the cost of creating a coordinate system and the cost for the experts are expensive. In addition, the results cannot detect damage inside the structure, and only when large movements occur will there be warnings of the need to repair the bridges [5; 6]. Most structural health monitoring solutions are based on assessing the structure's response to external stimuli. Based on this, several static testing methods have been implemented. Several researchers have used and implemented static data in their studies for damage detection [7; 8; 9; 10; 11; 12]. The principle is to use loads equal to or less than the design loads, such that they act on the structure. Information about the displacement and deformation at dangerous locations (or locations that need to be determined) can then be accurately identified. This method is intuitive, easy to understand, and highly reliable. However, logistics work requires a lot of money; some equipment is expensive, or can only be used once for a position (e.g., strain gauge). This method also has the disadvantage of necessitating the suspension of traffic from utilising the bridge during the experiment. This causes difficulties for bridges on important traffic routes with high traffic density, or bridges located in cities. To overcome the disadvantages of the visual inspection method, as well as the static test method, non-destructive structural damage detection techniques have been developed. Non-destructive health monitoring techniques can be classified as either global methods or local methods. Local methods focus on only part of the structure, and include ultrasonic or acoustic methods, magnet field methods, X-ray inspection. However, these methods, by merit of being local approaches, require prior information about the location of the damaged area [13]. One of the methods for monitoring global structural health is based on vibration measurements. Accordingly, it is more effective to use dynamic responses, such as degradation detection, to detect both abrupt and gradual changes. The dynamic measurement of responses requires the control of both environmental and operational impacts, in order to achieve accurate data.