Self-Charging and Self-Monitoring Smart Civil Infrastructure Systems: Current Practice and Future Trends
被引:7
作者:
Alavi, Amir H.
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h-index: 0
机构:
Univ Missouri, Dept Civil & Environm Engn, Columbia, MO 65211 USAZhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
Alavi, Amir H.
[2
]
Hasni, Hassene
论文数: 0引用数: 0
h-index: 0
机构:
Michigan State Univ, Dept Civil Engn & Environm Engn, E Lansing, MI 48823 USAZhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
Hasni, Hassene
[3
]
Jiao, Pengcheng
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R ChinaZhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
Jiao, Pengcheng
[1
]
Aono, Kenji
论文数: 0引用数: 0
h-index: 0
机构:
Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USAZhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
Aono, Kenji
[4
]
论文数: 引用数:
h-index:
机构:
Lajnef, Nizar
[3
]
Chakrabartty, Shantanu
论文数: 0引用数: 0
h-index: 0
机构:
Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USAZhejiang Univ, Ocean Coll, Zhoushan 316021, Zhejiang, Peoples R China
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.