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. • Deep learning-based algorithm is developed for Bridge Weigh-In-Motion system. • Validated FEA-based influence surfaces and strain gauge data are used as input. • BME-Simulated 1 (BME-S1) dataset with more than 100000 vehicle models is provided. • A BWIM system is developed with class B+ in accordance with COST 323 standard. [ABSTRACT FROM AUTHOR]