## 1 Introduction  
The dynamic parameters associated with a bridge are employed by vibration-based structural health monitoring (SHM) methods for damage to be either detected (known as 'level I' damage detection methods), detected and located ('level II' methods), or detected, located, and quantified ('level III' methods). There is also a 'level IV' group of methods that not only detects, locates and quantifies damage, but also estimates the remaining life of the structure [1]. These dynamic parameters can be divided into two categories [2, 3]:  
* Those directly related to the main modes of vibration of the structure such as natural frequencies, damping, mode shapes, frequency contour, nodes position, modal assurance criteria, mode shape curvature, modal strain energy, etc. ;
* Others such as coupling measurements, anti-resonances, frequency response functions, cumulative absolute velocity, cumulative absolute displacement, distributed vibration intensity, mean cumulative vibration intensity, etc. ;  
Accurate measurements of sophisticated dynamic parameters, i.e., mode shape curvature, come at a cost, and typically require sensors (i.e., accelerometers) installed at more locations and with a higher resolution than needed for extracting simpler parameters. For instance, a low number of accelerometers may be insufficient to capture a curvature change in the mode shape associated with the location of damage, but it can easily capture a shift in natural frequencies. The magnitude of this shift can be associated with a localized bending stiffness loss and will depend on the severity and the relative position of the stiffness loss within the mode shape. In theory, frequencies can be obtained by simulating damage at different locations on a mathematical model of the structure, and then comparing with measured frequencies to locate the damage. In practice, damage is difficult to characterize from frequencies alone due to the need to capture multiple modes, the sensitivity to noise, operational and environmental loading, the dependence on the selected damage and structural models, and the non-uniqueness of the solution. Therefore, civil engineering structures are in urgent need of reliable assessment methods based only on the first few modes with simple assumptions about the behavior of the structure [4]. If traffic was used as the excitation force and the bridge was model-led using a finite element approach based on linear elastic elements, the frequencies of the bridge in free vibration are related to the time-invariant mass, stiffness, and damping matrices of the bridge. Nevertheless, periods of forced vibration involving vehicle-bridge interaction (VBI) will be difficult to avoid in locations where heavy traffic occurs regularly. The frequency content of the response due to traffic will differ in free and forced vibration according to the mass and frequency ratios of the vehicle to the bridge [5; 6; 7]. Shifts in forced frequency are affected by damage to an extent that varies with the location of the vehicle on the bridge [8]. Recently, the concept of using a traffic fleet to monitor bridges has drawn the interest of many researchers. For instance, [PERSON] and [PERSON] [9] developed a level I damage detection technique that is based on the correlation between the weights measured by a pavement-based weigh-in-motion (WIM) system installed near the bridge, and the weights obtained by a bridge-based weigh-in-motion (BWIM) system for a specific truck fleet. WIM systems measure axle weights, spacings between axles, and the speed of the overpassing vehicles without interruption to the traffic flow. In the case of pavement-based systems, the sensors (i.e., piezoelectric, bending plate, load cell) are embedded in the road [10; 11; 12]. In the case of bridge-based systems, the sensors (i.e., strain gauges) are attached to the softf of a bridge deck. Traditional BWIM algorithms relate the measured response, usually strains, to the axle weights through the influence line [13].