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

被引:7
作者
Alavi, Amir H. [2 ]
Hasni, Hassene [3 ]
Jiao, Pengcheng [1 ]
Aono, Kenji [4 ]
Lajnef, Nizar [3 ]
Chakrabartty, Shantanu [4 ]
机构
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
[2] Univ Missouri, Dept Civil & Environm Engn, Columbia, MO 65211 USA
[3] Michigan State Univ, Dept Civil Engn & Environm Engn, E Lansing, MI 48823 USA
[4] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USA
来源
SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2019 | 2019年 / 10970卷
基金
美国国家科学基金会;
关键词
Civil Infrastructure Health Monitoring; Smart Cities; Energy Harvesting; Self-powered Sensing; Machine Learning; STEEL BRIDGE GIRDERS; FATIGUE CRACKING; BEAMS;
D O I
10.1117/12.2513476
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Next generation of smart infrastructure is heavily dependent on distributed sensing technology to monitor the state of urban infrastructure. The smart sensor networks should react in time, establish automated control, and collect information for intelligent decision making In this paper, we highlight our interdisciplinary research to address three main technical challenges related to smart infrastructure: (1) development of smart wireless sensors for civil infrastructure monitoring, (2) finding an innovative, cost-effective and sustainable energy resource for empowering heterogeneous, wireless sensor networks, and (3) designing advanced data analysis frameworks for the interpretation of the information provided by these emerging monitoring systems. More specifically, we focus on development of a self-powered piezo-floating-gate (PFG) sensor that uses only self-generated electrical energy harvested by piezoelectric transducers directly from a structure under vibration. The performance of this sensing technology is discussed for different civil infrastructure systems with complex behavior. Subsequently, the proposed data interpretation systems integrating deterministic, machine learning and statistical methods are reviewed. We outline our thoughtful vision for the proposed framework to serve as an integral part of future smart civil infrastructure, which will be capable of self-charging and the self-diagnosis of damage well in advance of the occurrence of failure.
引用
收藏
页数:12
相关论文
共 25 条
  • [1] Internet of Things-enabled smart cities: State-of-the-art and future trends
    Alavi, Amir H.
    Jiao, Pengcheng
    Buttlar, William G.
    Lajnef, Nizar
    [J]. MEASUREMENT, 2018, 129 : 589 - 606
  • [2] Fatigue cracking detection in steel bridge girders through a self-powered sensing concept
    Alavi, Amir H.
    Hasni, Hassene
    Jiao, Pengcheng
    Borchani, Wassim
    Lajnef, Nizar
    [J]. JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, 2017, 128 : 19 - 38
  • [3] Damage growth detection in steel plates: Numerical and experimental studies
    Alavi, Amir H.
    Hasni, Hassene
    Lajnef, Nizar
    Chatti, Karim
    [J]. ENGINEERING STRUCTURES, 2016, 128 : 124 - 138
  • [4] Continuous health monitoring of pavement systems using smart sensing technology
    Alavi, Amir H.
    Hasni, Hassene
    Lajnef, Nizar
    Chatti, Karim
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2016, 114 : 719 - 736
  • [5] An intelligent structural damage detection approach based on self-powered wireless sensor data
    Alavi, Amir H.
    Hasni, Hassene
    Lajnef, Nizar
    Chatti, Karim
    Faridazar, Fred
    [J]. AUTOMATION IN CONSTRUCTION, 2016, 62 : 24 - 44
  • [6] Damage detection using self-powered wireless sensor data: An evolutionary approach
    Alavi, Amir H.
    Hasni, Hassene
    Lajnef, Nizar
    Chatti, Karim
    Faridazar, Fred
    [J]. MEASUREMENT, 2016, 82 : 254 - 283
  • [7] [Anonymous], 2018, P 2018 IEEE 61 INT M
  • [8] Aono K, 2018, P GLSVLSI 18 MAY 23
  • [9] Aono K, 2016, IEEE INT SYMP CIRC S, P2058, DOI 10.1109/ISCAS.2016.7538983
  • [10] Aono Kenji, 2017, ANCRISST 13 INT WORK