## 1 Introduction
In these systems, the measurements from the healthy bridge condition are analysed to build and train a model [16]. [PERSON] et al. employed a multi-stage energy-based method for damage assessment that uses a wavelet packet transform and an artificial neural network (ANN) to detect local damages in steel frame structures [17]. A benchmark structure of the American Society of Civil Engineers (ASCE) was tested in a laboratory to validate the model. [PERSON] et al. proposed an extended Kalman filter-based ANN model for SHM that estimates the weights in a regression neural network to detect bridge damage under severe temperature changes [18]. They trained the neural network using one-year of measured bridge accelerations and temperature data using a real bridge to test the concept. Although the machine learning models have shown promising results, they require a great deal of bridge data to train the model effectively and are demanding of computational resources due to the large number of unknown parameters. Bridge weigh-in-motion (BWIM) is a technique for finding the gross vehicle and axle weights of trucks crossing a bridge, using a measured bridge [19; 20]. Any variation in the bridge structural condition (stiffness) changes its behaviour and manifests itself in the form of changes in inferred BWIM weights. Therefore, there have been some studies on the use of the BWIM system for bridge damage detection [9]. The BWIM technique was first proposed by [PERSON], who found the vehicle axle weights by minimising the sum of squared differences between the measured and calculated responses of a bridge [19]. The gross vehicle weights (GVWs) were then calculated by summing the inferred axle weights. BWIM systems generally consist of two sets of sensors: one for axle detection [21] and a second to obtain the responses used to infer the axle weights [22]. Traditional BWIM systems use strain responses which are measured by strain gauges, strain transducers or fibre Bragg grating (FBG) sensors [9; 23]. However, a BWIM system that uses strain measurements directly is not suitable for bridge monitoring. Many bridges are statically determinate (single span simply supported), and the bending moment is unaffected by stiffness. As a result, strain responses only change if the sensor is located at (or very close to) the damaged point. If the bridge is damaged at another location, strain is not affected, and any strain-based BWIM system becomes ineffective at detecting damage. [PERSON] et al. [24] used bridge deflection instead of strain, measured without using any sensors attached to the bridge, and developed a contactless bridge weigh-in-motion (cBWIM) system which has reasonably good accuracy for GVWs [25]. [PERSON] et al. proposed a portable BWIM system that uses acceleration rather than strain as the bridge response. It integrates the acceleration signals twice to estimate the displacements and uses this for the BWIM analysis [26]. In this integration process, the influence of initial conditions is significant, and this adversely affects the accuracy. Specifically, pre-existing vibrations may be present and may contaminate the calculations. To overcome this problem, large numbers of accelerometers are needed and, even then, accuracy is relatively poor. The effect temperature has on stiffness of concrete structures is a well-known phenomenon that influences all health monitoring techniques and needs to be understood for a perfect structural health monitoring system [27; 28]. Concrete bridges are always subject to change in stiffness due to temperature change as it affects the modulus of elasticity and compressive strength of concrete. Higher temperature levels cause linear degradation in the modulus of elasticity and compressive strength of concrete, which implies that any increase in temperature decreases the global stiffness of the bridge. Increases in temperature affect the chemical bonds of concrete paste, causing breaking and disintegration of concrete products and reducing the elastic modulus of concrete [26].