## 1 Introduction  
Bridges are critical components in traffic networks but may be damaged by corrosion or may be struck by a vehicle passing underneath. Monitoring of the structural health of bridges is integral to most transport infrastructure management programmes. While most bridges are still monitored by subjective visual inspection, electronic structural health monitoring (SHM) has attracted increasing attention in recent years. [PERSON] et al. [1] summarise a typical SHM process as (a) the definition of damage: local or global; (b) data acquisition, fusion, and cleaning: what kind of data is used and how; (c) feature extraction: what information is extracted from the data; and (d) statistical model development: how to model structural damage with the extracted information. The method presented in this paper infers gross vehicle weight (GVW) data from bridge acceleration signals and uses the standard deviation of the inferred GVW data to detect bridge global damage. Generally, structural damage refers to changes in the system which result in current or future adverse impacts on its performance [2]. Some data can show damage directly, such as crack width, but this requires advanced knowledge of where the damage is located. Other data, such as strains, deflections, rotations, and accelerations, can also be used as indirect damage indicators. [PERSON] and [PERSON] [3] identify several features that can be extracted from bridge vibration information, such as natural frequency, mode shape, modal curvature, dynamically measured flexibility, and more. Most researchers use accelerations to find bridge mode shapes and natural frequencies, which are used as indicators of damage in SHM [3, 4, 5]. However, the natural frequency is insensitive to damage, and mode shape generally requires large numbers of sensors. By using bridge vibration information, not only bridge properties but also passing vehicle weights can be extracted. [PERSON] and [PERSON] [6] showed that if there is an overall reduction in the bridge stiffness, the inferred vehicle weights increase, i.e., the more flexible bridge infers GVWs that are greater than the actual weights. The weights of passing vehicles can be obtained from recorded bridge monitoring data in a process known as Bridge Weigh in Motion (BWIM). The BWIM concept was introduced in the 1970s by [PERSON] [7] who proposed an algorithm to calculate weights from strain measurements on the bridge. [PERSON] et al. [8] summarise methods that use bridge measurements to find passing vehicle weights. Most BWIM methods use bridge strain or deflections. In practice, most deflection measurement methods require a reference point to measure from, normally under the bridge, which can be problematic as such a point is not always available (e.g., when the bridge is over water). Recently, a contactless BWIM system was proposed that used image analysis technology to measure bridge deflections and infer GVWs from them [9; 10]. The drawback of contactless BWIM is that the accuracy depends on the performance of the image collection equipment which is not generally sufficiently accurate. Also, it requires a known weight truck for setup or to recalibrate. Compared with deflection, acceleration measurement is inertial, so it does not require a reference point. Recently, some researchers have tried to use bridge acceleration to find passing vehicle weights. [PERSON] and [PERSON] [11] proposed an approach to use bridge acceleration to estimate bridge displacement by using a state vector estimation. The moving force identification (MFI) algorithm is then used to estimate vehicle weights. [PERSON] et al. [12] used an acceleration-based free vibration method to determine bridge displacement and then used the bridge influence line to calculate passing vehicle weights. The main problem with the approach, which uses acceleration to get bridge displacement, relates to knowing the bridge's initial conditions. As described by [PERSON] et al. [13] inadequate bridge initial conditions can cause large errors in estimated displacements.