# Development and Testing of a Railway Bridge Weigh-in-Motion System
## 4 Results and Discussion
The weights of the Train 2  
Figure 11: Error in gross weights of carriages (i) when train was assumed to have constant velocity and (ii) when carriage velocities were calculated separately: Trains 1 to 4.  
carriages were predicted very accurately, which can be linked to constant travelling speed and uniform distribution of carriage weights. In the results up to this point, all four trains were used to calibrate the system. As such, the mean weight is not an indication of the accuracy, but the low standard deviation indicates an excellent level of accuracy relative to conventional road weigh-in-motion technologies. To investigate the sensitivity of the algorithm to the chosen calibration/test trains, 11 permutations of the calibration database were considered using all possible combinations of the four trains for calibration. These combinations were then used to produce a linear regression model between predicted GVW using the revised RB-WIM algorithm and the measured GVWs of carriages. Using each regression model, the GVWs of carriages and locomotives for each train were calculated and compared to the corresponding static values (Figure 14). The light grey area in this figure shows the minimum and maximum range of errors for each regression model and the dark grey represents the mean of the errors in carriage GVW \(\pm\) one standard deviation (for each model). The horizontal dashed lines represent the minimum, maximum, and mean \(\pm\) one standard deviation. It can be seen that there was little difference in the results except for calibration using Trains 3 and 4, likely due to the poor accuracy of Train 4. According to the railway authorities in Poland, there is a very common 120 t locomotive that operates on the network. At the time of the measurements for this study, in addition to the four calibration trains, another two trains with locomotives of approximate 120 t gross vehicle mass were measured. In this figure, each point represents an individual locomotive/carriage and points aligned in the vertical direction (with small timestamp difference) represent one train. Figure 15 presents all measured trains and time of measurement. Both in the trains with 120 t locomotives and the others,  
Figure 12: Noise in speed measurement signals. Figure 13: Comparison between the original and revised algorithms: (a) mean error for each algorithm, (b) coefficient of variation of the error. there were carriages/locomotives weighing about 80 t with most of the remaining carriages ranging between 20 t and 50 t.  
Figure 14: Carriage GVW error for the regression models with different datasets and the revised algorithm considering variable carriage velocity. Figure 15: Gross vehicle mass of measured locomotives and carriages for four days of measurement. The highlighted sections represent the four calibration trains. Figure 16a illustrates a particular train with 40 carriages, where it can be seen that the front carriages were much more heavily loaded than the others. Figure 16b,c provide a closer view of two groups of carriages 2-23 and carriages 24-40, respectively. Apart from the locomotive, the front carriages all had weights between 74 and 81 t. There was a dramatic drop at Carriage 24 and all remaining carriages weighed between 20 t and 28 t.