## 1 Introduction
More complex BWIM algorithms apply moving force identification theory [14], but are difficult to implement in practice. [PERSON] et al. [15] explore the use of accelerations as input to a traditional BWIM algorithm to detect damage in a bridge from the weights inferred for a truck fleet. The algorithm is tested with one year of experimental data from a short-span reinforced concrete bridge in Slovenia. Further on, [PERSON] et al. [16] apply the same concept based on the acceleration response to detect global damage, but using a moving force identification algorithm to estimate the weights of the vehicles. Following the same principle of detecting damage from deviations in BWIM results, [PERSON] et al. [17] use a traditional static algorithm that adopts the rotation response of the bridge as input. The rotation of 2D and 3D numerical bridge models due to real traffic fleets from a WIM site in Virginia (US) (i.e., almost 160,000 5-axle trucks recorded over a period of 4 years) is used for assessment purposes. While the aforementioned publications deal with direct methods, i.e., measurements taken from sensors installed in the bridge, there is also an increasing interest in indirect methods such as vehicular measurements while crossing a bridge [18; 19; 20]. The latter is the line of research followed by [PERSON] and [PERSON], who derive the response of the contact point between the wheel and the bridge from sensors mounted on the vehicle for damage detection purposes [21]. The method is applied to a monitoring campaign using a vehicle fleet for a period of 2 years by means of theoretical simulations [22]. The authors were able to account for temperature effects and various vehicle speeds, although they considered the same vehicle in all the simulations, i.e., a fixed set of vehicle properties. As an example of practical implementation, smartphones located in regular traffic or public transport can be used to monitor a network of bridges [23; 24; 25], i.e., the vehicle fleet does not consist of specialized vehicles fitted with high-end sensors. Preliminary results show promise in terms of smartphones providing enough accuracy for the identification of frequencies. In this regard, the road profile is known to affect drive-by measurements; thus, its characterization can contribute to the increased accuracy of indirect methods. [PERSON] et al. [26] met this task by exploiting the data collected in multiple crossings of a vehicle fleet. Using numerical simulations, the authors were successful in extractingthe road profile without prior knowledge of the vehicle properties. A general view of the challenges and opportunities associated with the use of an Intelligent Transport System (ITS) to enhance the performance of SHM systems is provided by [PERSON] et al. [27]. Indirect monitoring in combination with ITS, i.e., traffic cameras and detectors, are presented as a potential precursor for connected vehicle technology, where wireless signals are used to transmit data between vehicles and/or to the bridge infrastructure. This paper aims to exploit the features contained in the forced response of the first mode of vibration that are not present in free vibration to develop a novel damage detection method. Bridge frequencies associated with the first mode are calculated in forced vibration for each position of a planar 3-axle truck on a bridge beam model using eigenvalue analysis of the coupled system matrix. These calculations cover multiple damage locations and severities, assuming that the damage is modeled as a crack that leads to a loss of bending stiffness at selected elements. Ideally, a relatively large sample of trucks from the operational traffic flow would be employed for gathering information about the bridge. For this reason, the analysis was extended to a truck fleet with a similar static configuration, but differing in dynamic properties.