###### Abstract
Firstly, aside from simply supported beams, the influence line functions for other, more complex bridge types can be challenging to calculate theoretically, often requiring finite element analysis to develop the necessary numerical models, which is time-consuming. Secondly, the influence line function-based method is limited to scenarios where only one vehicle is on the bridge, making it more appropriate for inspection rather than for online monitoring with multiple vehicles in operation. In contrast to the influence line-based method, another category of data-driven methods seeks to localize damage by identifying data features [14]. Unlike methods grounded in theoretical physics, this new class of approaches depends on implicit data training using cutting-edge machine learning techniques [15]. Typically, a database containing responses from both intact and damaged bridges is first established. With this database, machine learning algorithms are employed to create a mapping between the monitored bridge responses and the damage conditions. Ultimately, the responses from a target bridge are used as inputs for this mapping, with the outputs indicating the damage state [16]. However, there are two notable drawbacks to data-driven algorithms. First, the bridge in the database should match the target bridge to ensure that the features selected from the database are also applicable to the target bridge. Otherwise, due to variations in physical parameters such as support conditions, frequencies, and mode shapes across different bridges, the efficacy of these algorithms may be questionable. Second, it is challenging to obtain responses from damaged bridges, let alone those with varying degrees of damage. Consequently, much of this research relies on numerical simulations, which can limit their practical application in real-world engineering scenarios. Another widely employed data-driven method for damage localization is the mode shape identification technique [17]. The premise is that local damage will cause local alterations in the bridge's mode shape functions. Consequently, by identifying abnormalities in the mode shapes, it becomes feasible to pinpoint the location of damage. While several mode shape identification methods have been developed [18; 19], these approaches typically necessitate a substantial number of sensors, which can sometimes be challenging to deploy adequately. As an alternative to the aforementioned data-driven methods, response reconstruction has emerged in recent years as a potential method for damage localization [20; 21; 22; 23; 24; 25]. Thisapproach leverages historical monitoring data from multiple cross-sections to simulate a mapping relationship between the responses at these sections using machine learning techniques such as time-series prediction or time-invariant regression methods. This relationship is then used to reconstruct responses at a specific cross-section based on measured responses from other sections. If damage occurs around the specific cross-section, a significant divergence is expected between the reconstructed and measured responses. Given the limitations of current research [3; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], data reconstruction methods are posited as a promising solution for damage localization. Firstly, unlike model updating methods that rely on finite element models, this data-driven approach can achieve high efficiency in practical engineering applications without the need to construct any finite element models. Moreover, compared to the influence line-based method, data reconstruction has demonstrated effectiveness under real-world complex traffic scenarios with unknown numbers of moving vehicles. This suggests that online monitoring could become feasible. Furthermore, the response reconstruction method does not require measured data from damaged bridges. Also, previous studies [26; 27] have shown that this method is based on the natural physical properties of bridges and is likely applicable to various types of beam bridges.