Bolt load looseness measurement for slip-critical blind bolt by ultrasonic technique: a feasibility study with calibration and experimental verification

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-05-17 DOI:10.1177/14759217231173873
Xing Gao, Wei Wang
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Abstract

Preload is important for the performance of bolted connections, especially for high strength bolt like slip-critical blind bolt (SCBB). There have been relatively few studies focused on detecting the looseness of blind bolts prior to this research. This article proposes a method based on the acoustoelastic effect to monitor the change in the preload in the bolt and detect the relaxation from the initial preload. The technique is suitable for such blind bolted connection because it only needs to connect with one side of bolted connection, unlike some other bolted connection monitoring methods. Considering that for SCBB, the traditional acoustoelastic technique cannot be applied because it needs the unstressed state of the bolt as baseline. The relationship between looseness of bolt load and change of travelling time is deduced. The measuring objective is then changed to the looseness of bolt load, instead of the bolt load itself. The practical processes of calibration, real-time monitoring and periodical detection are proposed, considering the application on real construction site. The tests on different configurations of SCBBs prove the reliability of the ultrasonic technique based on change in time-of-flight.
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用超声技术测量滑移临界盲螺栓的螺栓载荷松动度:可行性研究,并进行了标定和实验验证
预紧力对螺栓连接的性能有着重要的影响,特别是对于像临界滑移盲螺栓(SCBB)这样的高强度螺栓。在此之前,针对盲螺栓松动检测的研究相对较少。本文提出了一种基于声弹效应的锚杆预紧力变化监测和预紧力松弛检测方法。与其他螺栓连接监测方法不同,该方法只需要连接螺栓连接的一侧,因此适用于盲螺栓连接。传统的声弹技术需要锚杆的无应力状态作为基线,因此无法应用于SCBB。推导了锚杆荷载松动度与行程时间变化的关系。然后将测量目标改为螺栓载荷的松动,而不是螺栓载荷本身。考虑到在实际施工现场的应用,提出了标定、实时监控和定期检测的具体流程。通过对不同配置的单轴单轴板的试验,验证了基于飞行时间变化的超声技术的可靠性。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
期刊最新文献
Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Deep learning-based concrete defects classification and detection using semantic segmentation. Combination of active sensing method and data-driven approach for rubber aging detection Distributed fiber optic strain sensing for crack detection with Brillouin shift spectrum back analysis An unsupervised transfer learning approach for rolling bearing fault diagnosis based on dual pseudo-label screening
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