## 1 Introduction  
With the rapid development of the economy, highway freight volume has followed a rapid growth, resulting in a traffic overload phenomenon becoming increasingly common. Overload will not only shorten the service life of bridges, but also significantly increase the probability of traffic accidents; severe threats to the safety of bridge structures may even be caused. The accurate identification of overload loads has important theoretical significance and engineering application value for structural health monitoring and safety assessment [1, 2]. To accurately and stably identify the traffic load distribution on the bridge deck, [PERSON] et al. [3] proposed an improved full-bridge traffic load monitoring method based on the YOLO-v3 convolutional neural network. The method adopted a training dual-target detection model to identify profiles of the vehicle and correct vehicle centroid for accurate vehicle location. [PERSON] et al. [4] proposed a computer vision-based method that simultaneously identifies the moving vehicle's load, the location of the vehicle on a bridge, and the displacement response, which are then used to update the finite element model in the monitored structural system. Currently, the main bridge vehicle load identification methods include dynamic response-based load identification, pavement weight-in-motion (WIM), and non-pavement bridge weight-in-motion (denoted as B-WIM). The dynamic response-based method identifies the vehicle load by solving the interaction force at the contact point between the vehicle and the bridge at any moment, through the vibration equation of the vehicle-bridge coupling system. This method's application is susceptible to many factors, such as pavement unevenness and vehicle speed; furthermore, the algorithm is very complex, with many system parameters [5; 6]. Pavement WIM requires sensors to be buried by excavating the pavement, which affects traffic. Moreover, the durability of the sensors is poor, due to long-term direct vehicle rolling [7]. B-WIM technology involves installing sensors at the bottom of the bridge to monitor and identify vehicle loads, such as for dynamic strain-based B-WIM systems. The installation and maintenance of the sensors are easy, low-cost, and do not damage the pavement [8; 9]. Thus, it is an up-and-coming load identification technology. The orthotropic deck steel box beam bridge with obvious local stresses and short influence lines is suitable for vehicle load identification using B-WIM technology [10; 11]. [PERSON] and [PERSON] [12] proposed that the ratio of strain response areas of different vehicles driving across the bridge equals the ratio of the total vehicle weight. [PERSON] et al. [13] also studied the correspondence between the strain response area and axle weight of large-span continuous steel truss bridge structures, in order to identify the gross weight of vehicles. [PERSON] et al. [14] proposed a dynamic iteration algorithm between [PERSON] algorithm and the measured influence line algorithm to correct the ill-conditioned problem of solving the equation. [PERSON] [15] used calibrated influence lines on site to calculate the axle weight, and he studied the influence of the choice of influence lines, the shape of the influence lines, and the sampling frequency on axle weight identification. These methods are primarily used for simply supported beam bridges, slab bridges, steel girder bridges, and truss bridges, and do not consider the impact of the transverse position of vehicles, i.e., the one-dimensional B-WIM system. Due to the prominent local force in the orthotropic deck, a change in a vehicle's transverse position on the bridge greatly influences load identification; thus, an analytical method needs to locate the transverse position of the vehicle in advance. [PERSON] [16] studied the two-dimensional B-WIM algorithm based on the influence surface and conducted experimental studies on integral slab bridges and girder bridges, respectively.