Department of Civil, Environmental and Architectural Engineering, The University of Kansas, Lawrence, KS 66045, USA.
Department of Civil, Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Sensors (Basel). 2022 Jul 6;22(14):5076. doi: 10.3390/s22145076.
This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.
本文提出了一种用于钢桥结构疲劳裂纹的结构健康监测（SHM）的现场实施。钢桥在重复交通荷载作用下会产生疲劳裂纹，这对其结构完整性构成了巨大威胁，并可能导致灾难性的失效。目前，对桥梁进行安全评估的准确、可靠的疲劳裂纹监测仍然是一项艰巨的任务。另一方面，无线智能传感器通过实现土木工程的长期模态识别，在全球范围内的 SHM 中取得了巨大成功。然而，由于缺乏专门用于疲劳裂纹监测的有效传感器和相关算法，长期对局部损伤（如疲劳裂纹）的现场监测一直受到限制。为了填补这一空白，本文提出了一种无线大面积应变传感器（WLASS）来测量大面积应变疲劳裂纹，并开发了一种有效的算法，将测量的大面积应变数据处理成可操作的信息。所提出的 WLASS 由一个软弹性电容器（SEC）组成，用于测量大面积结构表面应变，一个电容式传感器板，用于将 SEC 的信号转换为可测量的电压变化，以及一个商用无线智能传感器平台，用于基于触发的无线数据采集、远程数据检索和云存储。同时，开发的疲劳裂纹监测算法通过三个自动化步骤处理 WLASS 在交通荷载下获得的数据，包括（1）交通事件检测，（2）使用广义 Morse 小波（GM-CWT）和峰值识别的时频分析，以及（3）跟踪潜在疲劳裂纹扩展的改进裂纹扩展指数（CGI）。所开发的 WLASS 和算法为现场长期疲劳裂纹监测提供了一个完整的系统。基于 GM-CWT 的提出的时频分析算法可靠地提取脉冲交通事件的有效性通过数值研究进行了验证。随后，通过在美国堪萨斯城 KS 的一座钢公路桥上进行现场部署，对所开发的 WLASS 和算法进行了验证。
