Most of the tragedies on any bridge structure have been the cause of high-density crowd behavior as a response to trampling as well as the crushing scenario. Therefore, it is most important to monitor such unforeseen situations by sensing the load imposed on the bridge structures. This scenario may arise where crowd movement is huge on these types of bridges. Similarly, the fiber Bragg grating (FBG) is a promising technology for structural health monitoring applications. In this work, an Internet of Things based FBG optical sensing scheme is proposed to monitor real-time strain distribution throughout the bridge structures and localization of load imposed on the structure from a central control room. A suspension bridge model is designed by referring to a real bridge scenario and these FBG sensors are deployed to validate the proposed machine learning models. In this article, the performances of two machine learning strategies are discussed for the accurate estimation of load and its position by acquiring high sensitive FBG sensors signals at a very high data rate. The algorithms include K-nearest neighbor (KNN) and random forest (RF); which are applied on each sensing data source, and then validated using a prototype suspension bridge model integrated with three FBG sensors (1532 nm, 1538 nm, and1541 nm) on a single optical fiber cable.

中文翻译：

---

一种评估机器学习模型的实验方法，用于使用 FBG 传感器和物联网估计悬索桥上的负载分布

任何桥梁结构上的大多数悲剧都是高密度人群行为的原因，作为对踩踏和压碎场景的反应。因此，最重要的是通过感应施加在桥梁结构上的载荷来监测这种不可预见的情况。这种情况可能会出现在这些类型的桥梁上人群流动很大的情况下。同样，光纤布拉格光栅 (FBG) 是一种用于结构健康监测应用的有前途的技术。在这项工作中，提出了一种基于物联网的 FBG 光学传感方案，用于监测整个桥梁结构的实时应变分布以及中央控制室施加在结构上的载荷的定位。参考真实桥梁场景设计了悬索桥模型，并部署了这些 FBG 传感器来验证所提出的机器学习模型。在本文中，讨论了两种机器学习策略的性能，以通过以非常高的数据速率获取高灵敏度 FBG 传感器信号来准确估计负载及其位置。算法包括K近邻（KNN）和随机森林（RF）；将其应用于每个传感数据源，然后使用在单根光纤电缆上集成三个 FBG 传感器（1532 nm、1538 nm 和 1541 nm）的原型悬索桥模型进行验证。讨论了两种机器学习策略的性能，以通过以非常高的数据速率获取高灵敏度 FBG 传感器信号来准确估计负载及其位置。算法包括K近邻（KNN）和随机森林（RF）；将其应用于每个传感数据源，然后使用在单根光纤电缆上集成三个 FBG 传感器（1532 nm、1538 nm 和 1541 nm）的原型悬索桥模型进行验证。讨论了两种机器学习策略的性能，以通过以非常高的数据速率获取高灵敏度 FBG 传感器信号来准确估计负载及其位置。算法包括K近邻（KNN）和随机森林（RF）；将其应用于每个传感数据源，然后使用在单根光纤电缆上集成三个 FBG 传感器（1532 nm、1538 nm 和 1541 nm）的原型悬索桥模型进行验证。