TY - GEN
T1 - Self-charging and self-monitoring smart civil infrastructure systems
T2 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
AU - Alavi, Amir H.
AU - Hasni, Hassene
AU - Jiao, Pengcheng
AU - Aono, Kenji
AU - Lajnef, Nizar
AU - Chakrabartty, Shantanu
N1 - Funding Information:
The presented work is supported by a research grant from the Federal Highway Administration (FHWA; Grant DTFH61-13-H00009). This material is based upon work supported by the National Science Foundation under Grant Nos. CNS-1646380, DGE-0802267 and DGE-1143954. P.J. acknowledges the Startup Foundation of the Hundred Talents Program at the Zhejiang University.
Funding Information:
The presented work is supported by a research grant from the Federal Highway Administration (FHWA; Grant DTFH61‐13‐H00009). This material is based upon work supported by the National Science Foundation under Grant Nos. CNS-1646380, DGE-0802267 and DGE-1143954. P.J. acknowledges the Startup Foundation of the Hundred Talents Program at the Zhejiang University.
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Civil Infrastructure Health Monitoring
KW - Energy Harvesting
KW - Machine Learning
KW - Self-powered Sensing
KW - Smart Cities
UR - http://www.scopus.com/inward/record.url?scp=85068337935&partnerID=8YFLogxK
U2 - 10.1117/12.2513476
DO - 10.1117/12.2513476
M3 - Conference contribution
AN - SCOPUS:85068337935
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
A2 - Lynch, Jerome P.
A2 - Huang, Haiying
A2 - Sohn, Hoon
A2 - Wang, Kon-Well
PB - SPIE
Y2 - 4 March 2019 through 7 March 2019
ER -