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Structural health monitoring system of a cable-stayed bridge using a dense array of scalable smart sensor network

机译:斜拉桥的结构健康监测系统,使用密集的可扩展智能传感器网络阵列

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This paper presents a structural health monitoring (SHM) system using a dense array of scalable smart wireless sensor network on a cable-stayed bridge (Jindo Bridge) in Korea. The hardware and software for the SHM system and its components are developed for low-cost, efficient, and autonomous monitoring of the bridge. 70 sensors and two base station computers have been deployed to monitor the bridge using an autonomous SHM application with consideration of harsh outdoor surroundings. The performance of the system has been evaluated in terms of hardware durability, software reliability, and power consumption. 3-D modal properties were extracted from the measured 3-axis vibration data using output-only modal identification methods. Tension forces of 4 different lengths of stay-cables were derived from the ambient vibration data on the cables. For the integrity assessment of the structure, multi-scale subspace system identification method is now under development using a neural network technique based on the local mode shapes and the cable tensions.
机译:本文介绍了一种结构健康监测(SHM)系统,该系统在韩国的斜拉桥(Jindo Bridge)上使用密集的可扩展智能无线传感器网络阵列。 SHM系统及其组件的硬件和软件是为低成本,高效且自主地监控桥梁而开发的。考虑到恶劣的室外环境,已经部署了70个传感器和两台基站计算机来使用自主SHM应用程序监视桥梁。系统的性能已在硬件耐用性,软件可靠性和功耗方面进行了评估。使用仅输出的模态识别方法,从测得的3轴振动数据中提取3-D模态特性。根据电缆上的环境振动数据,可以得出4种不同长度的电缆的拉力。为了对结构进行完整性评估,目前正在使用基于局部模式形状和电缆张力的神经网络技术开发多尺度子空间系统识别方法。

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