Bridge Damage Detection Approach Using a Roving Camera Technique.
Bridge Damage Detection Approach Using a Roving Camera Technique.
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DOI：
10.3390/s21041246
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发表时间：
2021-02-10
期刊：
影响因子：
--
通讯作者：
Taylor S
中科院分区：
文献类型：
--
作者：
Lydon D;Lydon M;Kromanis R;Dong CZ;Catbas N;Taylor S
极端气候事件的增加、交通模式的加剧以及长期投资不足，导致我们的公路和铁路运输网络中的桥梁日益恶化。结构健康监测（SHM）系统提供了一种客观捕获和量化运行条件下的劣化的方法。计算机视觉技术由于能够使用非接触方法在远距离获得位移数据，在SHM领域受到了相当大的关注。此外，它还提供了一种低成本、快速的仪器解决方案，对结构的正常运行干扰小。然而，即使是在中等跨度桥梁的情况下，需要许多摄像机来捕捉全球的反应可能成本过高。本研究提出了一种漫游相机技术来捕捉实验室模型桥梁在活载作用下的响应的完整推导，以识别桥梁的损伤。将位移作为合适的损伤指标，采用两种方法对实验室桥梁模型中边界条件变化下的整体位移变化幅度进行评估。从这项研究中可以看出，两种方法都可以在模拟模型中检测到损伤，从而提供了一种不需要复杂传感器安装的SHM解决方案。
Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.
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影响因子：
4.3
作者：
Fukuda, Yoshio;Feng, Maria Q.;Tanaka, Takayuki
通讯作者：
Tanaka, Takayuki
影响因子：
8.4
作者：
Dilena, M.;Limongelli, M. P.;Morassi, A.
通讯作者：
Morassi, A.
DOI：
10.12989/smm.2018.5.2.273
发表时间：
2018-06-01
期刊：
STRUCTURAL MONITORING AND MAINTENANCE
影响因子：
--
作者：
Celik, Ozan;Terrell, Thomas;Catbas, F. Necati
通讯作者：
Catbas, F. Necati
影响因子：
4.7
作者：
Abdeljaber, Osama;Avci, Onur;Inman, Daniel J.
通讯作者：
Inman, Daniel J.
影响因子：
3.9
作者：
Dong, Chuan-Zhi;Celik, Ozan;Taylor, Su
通讯作者：
Taylor, Su