Institute of Science, Engineering and Technology (ICET), Federal University of the Jequitinhonha and Mucuri Valleys (UFVJM), Teófilo Otoni 39803-371, Brazil.
Materials Testing Institute (MPA), University of Stuttgart, 70569 Stuttgart, Germany.
Sensors (Basel). 2021 Apr 19;21(8):2871. doi: 10.3390/s21082871.
The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.
对现有结构的结构状况进行跟踪的能力是桥梁所有者和运营商的主要关注点之一。在桥梁维护计划的背景下，目视检查目前是主要信息来源。然而，仅目视检查不足以满足当前的安全评估需求。从这个角度来看，近年来已经开发了大量关于结构健康监测的研究。然而，从实验室实验到实际应用的转换率仍然不尽人意。本文讨论了限制实时结构健康监测系统（SHM）部署和接受的主要限制，并提出了一种基于随机变量相关的实时分析算法，用于状态监测。该算法旨在自动响应，以检测多种随机动态负载下的局部结构故障等意外事件。研究结果是 SHM 项目的一部分，其中在德国 Neckarsulm 的一座预应力混凝土桥上安装了基于长标距光纤布拉格光栅传感器（LGFBG）的高传感器计数监测系统。作者还介绍了为处理大量数据而开发的数据管理系统，并展示了实施的后处理方法之一，即主成分分析（PCA）的结果。结果表明，所部署的 SHM 系统成功地将大量原始数据转化为有意义的信息。所提出的实时分析算法提供了一个可靠的通知系统，使桥梁管理者能够跟踪意外事件，作为决策的基础。
