Deep Sensing Approach to Single-Sensor Vehicle Weighing System on Bridges

• Published in: IEEE sensors journal 2019-01, Vol.19 (1), p.243-256
• Main Authors: Kawakatsu, Takaya, Aihara, Kenro, Takasu, Atsuhiro, Adachi, Jun
• Format: Article
• Language: English
• Subjects: Accelerometers; Artificial neural networks; Axles; Bridge circuits; bridge weigh-in-motion; Bridges; Capacitive sensors; Deep neural network; Feature extraction; Sensors; Signal detection; Strain; Structural health monitoring; Synchronism; Traffic surveillance; Vehicles; Weighing in motion
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2018.2872839

Bridge weigh-in-motion (BWIM) is a technique for detecting overloaded vehicles crossing a bridge without requiring them to stop. It may also be useful for monitoring the structural health of the bridge itself. To achieve accurate weighing of each vehicle, its properties, such as speed, locus, and wheel positions, should be estimated in advance. Conventionally, such information has been obtained via additional sensors such as cameras or via peak-signal detection, using multiple sensors installed across the bridge. This may require substantial computational resources or expensive synchronization between sensors, and the complexity of the overall BWIM system may lead to frequent breakdowns. In this paper, we propose a single-sensor-based BWIM system that utilizes a deep neural network. First, a vehicle's properties are obtained via feature extraction from the bridge strain response, as sampled by a single strain sensor. BWIM is then performed, using the same response data. The model parameters for vehicle detection are optimized automatically by consulting a surveillance camera while obtaining ground-truth data for a large number of vehicles crossing the bridge. After the model is optimized for the target bridge, the camera may be removed. Our proposal paves the way toward low-cost, compact, and single-sensor BWIM systems.