Synchronized IoT Framework for Vibration-Based Structural Health Monitoring of Suspension Bridges

IF 4.6 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Muhammad Hassan;Amr A. Nassr;Mohamed AbdelRaheem
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引用次数: 0

Abstract

This article presents the design, implementation, and experimental validation of an Internet-of-Things (IoT)-based structural health monitoring (SHM) system for suspension bridges using synchronized vibration measurements. The proposed framework integrates distributed micro-electro-mechanical systems (MEMSs)-based sensing nodes, global positioning system (GPS)-assisted time synchronization, low-power wireless communication, and cloud-based modal analysis to support continuous monitoring of structural conditions. The main contribution of this work is the end-to-end integration of synchronized low-power IoT sensing, multitier wireless communication, and cloud-based vibration analysis within a unified SHM framework, together with experimental validation on a laboratory-scale suspension bridge. In the proposed system, each leaf node incorporates a high-accuracy digital accelerometer, ESP-NOW communication, and a duty-cycled sleep mode to reduce power consumption, while a central gateway aggregates the acquired data and transmits them to a remote server through a cellular network. Natural-frequency analysis, mode-shape evaluation, and the modal assurance criterion (MAC) are employed to assess changes in structural dynamic behavior. The system was evaluated using a laboratory-scale suspension bridge instrumented with five synchronized IoT sensing nodes and subjected to controlled shake-table excitations. Four hanger- and main-cable-damage scenarios were introduced to evaluate the overall operation of the proposed framework. The results demonstrate successful synchronized vibration acquisition, reliable wireless data transmission, and the ability to capture damage-sensitive modal changes, including natural-frequency shifts, mode-shape distortions, and MAC variations under different damage conditions. Communication experiments further show that ESP-NOW reduces energy consumption compared with transmission control protocol/internet protocol (TCP/IP) over Wi-Fi, extending node battery lifetime by approximately 40% through sleep-mode operation. Overall, the proposed SHM framework provides a cost-effective, energy-efficient, and synchronized IoT solution for vibration-based monitoring of suspension bridges under laboratory conditions. Future work will focus on field validation on real bridge structures and long-term operation under practical environmental conditions.
基于振动的悬索桥结构健康监测同步物联网框架
本文介绍了采用同步振动测量的基于物联网(IoT)的悬索桥结构健康监测(SHM)系统的设计、实现和实验验证。该框架集成了基于分布式微机电系统(mems)的传感节点、全球定位系统(GPS)辅助的时间同步、低功耗无线通信和基于云的模态分析,以支持对结构状况的连续监测。这项工作的主要贡献是在统一的SHM框架内将同步低功耗物联网传感、多层无线通信和基于云的振动分析端到端集成,并在实验室规模的悬索桥上进行实验验证。在提出的系统中,每个叶节点都包含一个高精度数字加速度计、ESP-NOW通信和一个占空比睡眠模式,以降低功耗,而中央网关聚合采集的数据并通过蜂窝网络将其传输到远程服务器。采用固有频率分析、模态振型评估和模态保证准则(MAC)来评估结构动力性能的变化。该系统在实验室规模的吊桥上进行了评估,吊桥上安装了五个同步的物联网传感节点,并受到可控振动台的激励。介绍了四种悬挂器和主缆损坏情况,以评估拟议框架的整体运行情况。结果表明,该系统成功实现了同步振动采集、可靠的无线数据传输,并能够捕捉损伤敏感模态变化,包括不同损伤条件下的固有频率位移、模态形状畸变和MAC变化。通信实验进一步表明,与Wi-Fi上的传输控制协议/互联网协议(TCP/IP)相比,ESP-NOW降低了能耗,通过睡眠模式操作将节点电池寿命延长了约40%。总体而言,所提出的SHM框架为实验室条件下基于振动的悬索桥监测提供了一种经济高效、节能且同步的物联网解决方案。未来的工作将集中在实际桥梁结构的现场验证和实际环境条件下的长期运行。
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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