Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%.

中文翻译：

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用于改进铁路桥梁罢工检测的并行异构数据融合卷积神经网络

低净空铁路桥为货运和客运列车提供重要的交叉口，并且容易受到超高车辆或设备的频繁撞击。碰撞检测系统可以帮助确保铁路桥梁及其使用者的安全；此类系统通过向负责在罢工后评估桥梁的铁路管理人员提供近乎实时的罢工通知来简化监控工作。本文开发了并行异构数据融合卷积神经网络（PHD-CNN），该网络对从在用铁路桥梁收集的数据进行操作，以改进超高车辆的检测和分类。卷积神经网络 (CNN) 自动从不同传感器模式的多个数据流中提取特征。该方法提供了一种机制来均匀化和融合不同的数据流，以用作分类器的输入，以区分桥梁撞击和经过的火车。该研究还通过框架敏感性表征提供了实用的实施指南，以检查输入数据流类型、数据集大小和 CNN 架构复杂性对性能的影响。最佳网络平均可检测 95% 的桥梁撞击，误报率低于 2%。