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Self-Charging and Self-Monitoring Smart Civil Infrastructure Systems: Current Practice and Future Trends

机译:自我充电和自我监控的智能民用基础设施系统:当前的实践和未来的趋势

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摘要

Next generation of smart infrastructure is heavily dependent on distributed sensing technology to monitor thestate of urban infrastructure. The smart sensor networks should react in time, establish automated control, and collectinformation for intelligent decision making. In this paper, we highlight our interdisciplinary research to address threemain technical challenges related to smart infrastructure: (1) development of smart wireless sensors for civilinfrastructure monitoring, (2) finding an innovative, cost-effective and sustainable energy resource for empoweringheterogeneous, wireless sensor networks, and (3) designing advanced data analysis frameworks for the interpretationof the information provided by these emerging monitoring systems. More specifically, we focus on development of aself-powered piezo-floating-gate (PFG) sensor that uses only self-generated electrical energy harvested bypiezoelectric transducers directly from a structure under vibration. The performance of this sensing technology isdiscussed for different civil infrastructure systems with complex behavior. Subsequently, the proposed datainterpretation systems integrating deterministic, machine learning and statistical methods are reviewed. We outlineour thoughtful vision for the proposed framework to serve as an integral part of future smart civil infrastructure, whichwill be capable of self-charging and the self-diagnosis of damage well in advance of the occurrence of failure.
机译:下一代智能基础设施严重依赖分布式传感技术来监视城市基础设施的状况。智能传感器网络应及时做出反应,建立自动控制,并收集信息以进行智能决策。在本文中,我们着重介绍我们的跨学科研究,以解决与智能基础设施相关的三个主要技术挑战:(1)开发用于民用\ r \ n基础设施监控的智能无线传感器,(2)寻找一种创新的,具有成本效益的,可持续能源,以增强异构无线传感器网络的能力,以及(3)设计高级数据分析框架,以解释这些新兴监测系统提供的信息。更具体地说,我们专注于开发自供电的压电浮栅(PFG)传感器,该传感器仅使用自压电换能器直接从振动结构中收集的自发电电能。该传感技术的性能仅针对具有复杂行为的不同民用基础设施系统进行讨论。随后,将对所提出的结合确定性,机器学习和统计方法的数据\ r \ n解释系统进行审查。我们概述了拟议框架的思想构想,该构想将成为未来智能民用基础设施的组成部分,该构架将能够在发生故障之前很好地进行自我充电和自我诊断。

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  • 会议地点 0277-786X;1996-756X
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    Department of Civil and Environmental Engineering, University of Missouri, Columbia,Missouri 65211, USA alavia@missouri.edu;

    Department of Civil Engineering Environmental Engineering, Michigan State University, EastLansing, MI 48823, USA;

    Ocean College, Zhejiang University, Zhoushan 316021, Zhejiang, China pjiao@zju.edu.cn;

    Department of Computer Science and Engineering, Washington University in St. Louis, St.Louis, MO 63130, USA;

    Department of Civil Engineering Environmental Engineering, Michigan State University, EastLansing, MI 48823, USA;

    Department of Computer Science and Engineering, Washington University in St. Louis, St.Louis, MO 63130, USA;

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