A filtered measured influence line approach to bridge weigh-in-motion

In Bridge Weigh-in-Motion (B-WIM), an instrumented bridge is used as a scales to weigh passing trucks and their axles. The most common algorithm upon which modern B-WIM systems are based remains that developed by Moses (1979). The performance of this method is well documented; it is very good at estimating Gross Vehicle Weight, but less accurate for individual axles, particularly closely spaced axles on longer bridges. Many alternatives to Moses's original algorithm have been tested and some show the potential to improve accuracy but commercially available B-WIM systems are still based substantially on the original approach. This paper proposes a method of altering the B-WIM algorithm to improve the accuracy of the predictions. The measured dynamic signal, to which the algorithm is applied, is first filtered to remove high frequency components of the dynamic increment of load. The influence line used by the algorithm is also calculated differently. As previously described by OBrien et al. (2006) it is determined using a pre-weighed calibration truck and an algorithm to automatically convert the corresponding measured signal into a 'measured' influence line. However, for this work, the measured signal is first filtered to remove much of the high frequency dynamic components which results in a significant improvement in the overall accuracy of the system. Moses's equations are applied as in most other B-WIM systems but, in this case, using a filtered measured influence line and a filtered signal for the unknown truck. In this way, Moses's least squares fitting method is now comparing only the low frequency components of the measured and theoretical responses and produces a much more accurate fit. The new approach is tested in numerical models and it is shown to result in a substantial improvement in accuracy.