# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 2 Materials and Methods
### The Method of Modular WIM Systems  
The modular WIM system consists of three main parts: a sensor module to obtain weighing signals, a data acquisition module to send signals, and a master control module to calculate the number of axles, axle loads, and gross vehicle weight. The innovative design of the sensor module is a key feature that makes the modular WIM system different from other WIM systems. Its structure is shown in Figure 2. Along the traveling direction of the vehicle, there are a vehicle separator and N groups of weighing platforms, and each weighing module contains four bridge strain weighing sensors installed at the four corners of the platform. Weighing platforms operate independently, meaning that each platform can weigh on its own, or that multiple connected platforms can be combined to achieve weighing. The cascade structure design can help achieve two goals: one is continuous weighing with multiple vehicles traveling through platforms simultaneously, and the second is the whole-vehicle-load measurement of common vehicles.  
Figure 1: Overall architecture of the model.  
Figure 2: Sensor module structure diagram.  
To enable continuous weighing with multiple vehicles traveling through platforms simultaneously, each weighing platform is designed to be 3.6 m, configured in a cascade structure. Thus, the probability of different vehicles having axles on the same weighing platform is low. The system uses a vehicle separator to obtain the occupied platforms for vehicles, enabling the combination of WIM sensor signals. Each vehicle is weighed on the platform it occupies without interference, thereby significantly improving the traffic efficiency of freight vehicles at toll stations. The schematic of the vehicle's continuous weighing with multiple vehicles traveling through platforms simultaneously is shown in Figure 3.  
By changing the number N of the weighing platforms, the total length of the weighing platform is adjusted to realize whole-vehicle-load weighing. Typically, the number of weighing platforms N \(\leq\) 6. When N = 6, the total length of the weighing platform is 21.6 m, which exceeds the length of the conventional trucks. This configuration ensures sufficient weighing time for each vehicle while enhancing the weighing accuracy.  
Simultaneously, on the master control module, the effective weighing signal is a step and stabilization process, as shown in Figure 4b. As each axle enters and exits the scale platform, the signal increases and decreases, respectively. Therefore, it is essential to determine the time points of each axle entering and exiting the scale platform. Traditional methods include the slope segmentation algorithm based on Kalman smoothing and single most likelihood replacement. In this paper, we use an algorithm based on the Transformer-GRU Model to measure time points, and then obtain the number of axles and the gross vehicle weight.  
Figure 4: (**a**) Input-label sequence for axle recognition model; (**b**) combined weighing signal and axle recognition signals.  
Figure 3: Multiple vehicles travel through platforms simultaneously and measure vehicle weight.