###### Abstract  
Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle-bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization. with a specific damage index. Through the inverse analysis of system outputs, system characteristics (frequency, mode shape, damping ratio, etc.) would be estimated, and then the potential damage can be localized by detecting abnormalities in these characteristics or damage index. General damage localization methods fall into two categories: finite element model-based methods [6] and data-driven methods [7]. Finite element model-based methods are commonly applied to medium-span and short-span bridges. A direct model-based approach for damage localization is model updating [8]. For a specific bridge, numerous finite element models would be constructed. Utilizing corresponding algorithms, model updating processes are carried out to adjust bridge parameters in order to align simulation results with measured bridge responses. Potential damage can then be localized using the updated finite element models. However, this method is quite time-consuming due to the multitude of variables involved in bridge parameters. Additionally, it requires comprehensive design information for the given bridge, which may not always be readily available. Instead of model-based methods, some researchers have proposed data-driven approaches [9]. The underlying principle is to first establish a baseline of the responses for an intact bridge. Subsequently, monitoring data are analyzed to determine if the bridge's responses exceed established thresholds. For simply supported beams, researchers often use influence line functions as the baseline [10], which can be easily obtained through theoretical analysis. Additionally, theoretical models such as the rotational spring model [11] and the local stiffness reduction model [12] have been introduced to explain the behavior of a bridge with damage. According to these models, damage localization can be achieved by identifying abnormal local peaks on the measured influence line functions [13]. However, this method faces two main challenges.