## 1 Introduction
There is a consensus in the literature that the relationship between the concrete modulus of elasticity and temperature is linear [27; 28], but there is no consensus on the slope of the linearity. Similar to concrete, glass material experiences mechanical degradation due to long-term changes in humidity and temperature, which affect the dynamic parameters of the structure [29]. [PERSON] illustrated the effect of changing ambient conditions on bridge frequencies using a glass suspension footbridge [29]. [PERSON] et al. used a glass walkway structure to demonstrate the correlation between the bridge frequencies and vibration comfort level [30]. In recent years, the application of smart sensors has gained interest among many researchers. [PERSON] et al. summarized the literature on smart SHM techniques for long-term monitoring of long-span arch bridges [31]. Bridge strain and acceleration responses have been applied to a monitoring system [31] commonly used to monitor traffic loading and extract modal parameters and bridge frequencies to detect damage, respectively. [PERSON] et al. also conducted a case study with the application of five integrated subsystems for a smart SHM and assessed the advantages and disadvantages of various signal processing techniques on bridge acceleration and strain responses [31]. They monitored a long-span bridge in China for changing environmental and traffic load effects using an extensive layout of fibre optic sensors and environmental sensors to develop an effective long-term SHM system. Although the systems discussed in [31] have been found to be reasonably effective, they require a high number of sensors on the bridge which makes the installation time-consuming and expensive. In this paper, a novel smart approach to bridge damage detection, using a statistical analysis of acceleration-based BWIM results, is proposed which not only identifies damage (Level I) but also quantifies the extent of damage in the bridge. The acceleration responses are used directly to infer the GVWs. This means that the proposed SHM system is able to monitor traffic loading as well as bridge condition. A statistical analysis of the inferred GVWs allows the detection of damage. This approach is shown to work with only one acceleration measurement, which can be measured using a portable accelerometer, and information on axle configuration. Therefore, it can eliminate the requirement for many sensors to be installed on the bridge. This will not only make the SHM system cost effective but will also reduce the time to setup the system on the bridge. It is known that bridge accelerations are sensitive to damage, i.e., loss of bridge stiffness [32; 33]. Hence, a system that can reduce the number of sensors will decrease the cost as well as time of installation. A statistical approach is applied to the results to develop an effective method of bridge damage detection. To test the concept,a one-dimensional vehicle model is simulated on a simply supported beam with a class 'A' road profile [34] to obtain the bridge acceleration responses. Global stiffness loss due to a change in the bridge modulus of elasticity is used as bridge damage. The BWIM results are calculated for various values of vehicle parameters, and the statistical properties of the results are reviewed. Field measurements on a reinforced concrete bridge in Slovenia are used as a complementary test of the concept.