A strain gauge-based Bridge Weigh-In-Motion system using deep learning

Several breakthroughs have appeared in different applications due to deep learning which can be applied in Bridge Weigh-In-Motion (BWIM) systems as well. Therefore, deep learning applications should be examined in BWIM systems. However, some deep learning-based solutions have already been proposed in the international literature, they cannot be compared to static methods because available datasets are not appropriate to train and test artificial neural networks. In addition, numerous other aspects have been already considered during tests. In the current paper, a numerically simulated and validated comprehensive dataset is provided. It is designed to meet requirements of incremental development. This dataset makes it possible to benchmark static algorithms and deep learning-based methods on the same dataset. In the second part of the paper, a deep learning-based solution is proposed. The developed solution shows promising results on the provided dataset and also demonstrates the applicability of the dataset.

Automated summary

This paper explores the application of deep learning in Bridge Weigh-In-Motion systems and provides a comprehensive dataset for benchmarking static algorithms and deep learning-based methods. It also proposes a deep learning-based solution that demonstrates promising results on the dataset and the applicability of the dataset.