Department of Civil and Environmental Engineering, University of Illinois, 205 N. Matthews Ave, Urbana, IL 61801, USA.
Sensors (Basel). 2023 Mar 22;23(6):3330. doi: 10.3390/s23063330.
Railroads are a critical part of the United States' transportation sector. Over 40 percent (by weight) of the nation's freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billion of freight in 2021. A vital part of the freight network is railroad bridges, with a good number being low-clearance bridges that are prone to impacts from over-height vehicles; such impacts can cause damage to the bridge and lead to unwanted interruption in its usage. Therefore, the detection of impacts from over-height vehicles is critical for the safe operation and maintenance of railroad bridges. While some previous studies have been published regarding bridge impact detection, most approaches utilize more expensive wired sensors, as well as relying on simple threshold-based detection. The challenge is that the use of vibration thresholds may not accurately distinguish between impacts and other events, such as a common train crossing. In this paper, a machine learning approach is developed for accurate impact detection using event-triggered wireless sensors. The neural network is trained with key features which are extracted from event responses collected from two instrumented railroad bridges. The trained model classifies events as impacts, train crossings, or other events. An average classification accuracy of 98.67% is obtained from cross-validation, while the false positive rate is minimal. Finally, a framework for edge classification of events is also proposed and demonstrated using an edge device.
铁路是美国交通系统的重要组成部分。超过 40%（按重量计）的美国货物是通过铁路运输的，根据运输统计局的数据，2021 年铁路运输了 1865 亿美元的货物。货运网络的一个重要组成部分是铁路桥梁，其中相当数量的桥梁是净空较低的桥梁，容易受到超高车辆的撞击；这种撞击会对桥梁造成损坏，并导致其使用中断。因此，检测超高车辆的撞击对于铁路桥梁的安全运行和维护至关重要。虽然之前已经发表了一些关于桥梁撞击检测的研究，但大多数方法都使用更昂贵的有线传感器，并且依赖于简单的基于阈值的检测。挑战在于，使用振动阈值可能无法准确区分撞击和其他事件，例如常见的火车经过。在本文中，开发了一种使用基于事件触发的无线传感器进行准确撞击检测的机器学习方法。神经网络使用从两个仪器化铁路桥梁收集的事件响应中提取的关键特征进行训练。训练后的模型将事件分类为撞击、火车经过或其他事件。通过交叉验证获得了 98.67%的平均分类准确率，而误报率很小。最后，还提出并演示了一种基于边缘的事件分类框架，使用边缘设备实现。
