scholarly journals Ubiquitous Piezoelectric Sensor Network (UPSN)-Based Concrete Curing Monitoring for u-Construction

10.5772/22993 ◽  
2011 ◽  
Author(s):  
Seunghee Park ◽  
Dong-Jin Kim
Keyword(s):  
Sensor Network ◽  
Piezoelectric Sensor

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

2021 ◽  
pp. 147592172110332
Author(s):  
Mehrdad Ghyabi ◽  
Hamidreza Nemati ◽  
Ehsan Dehghan-Niri
Keyword(s):  
Finite Element ◽  
Sensor Network ◽  
Health Monitoring ◽  
Surface Crack ◽  
Piezoelectric Sensor ◽  
Network Coverage ◽  
Coverage Area ◽  
Fem Simulations ◽  
Network Geometry ◽  
Critical Defect

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

A Large-scale and Lightweight Piezoelectric Sensor Network for Aircraft Smart Composite Skin

2019 ◽  
Author(s):  
YU WANG ◽  
LEI QIU ◽  
YIJIE LUO ◽  
RUI DING
Keyword(s):  
Sensor Network ◽  
Large Scale ◽  
Piezoelectric Sensor ◽  
Smart Composite

scholarly journals Hypervelocity impact induced shock acoustic emission waves for quantitative damage evaluation using in situ miniaturized piezoelectric sensor network

2019 ◽  
Vol 32 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
Menglong LIU ◽  
Qiang WANG ◽  
Qingming ZHANG ◽  
Renrong LONG ◽  
Fangsen CUI ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Sensor Network ◽  
Piezoelectric Sensor ◽  
Hypervelocity Impact ◽  
Damage Evaluation

Life-cycle health monitoring of composite structures using piezoelectric sensor network

2021 ◽  
Author(s):  
yinghong yu ◽  
Xiao Liu ◽  
jun li ◽  
Yishou Wang ◽  
xinlin qing
Keyword(s):  
Life Cycle ◽  
Sensor Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Composite Structures ◽  
Large Scale ◽  
Structural Integrity ◽  
Piezoelectric Sensor ◽  
Curing Process ◽  
Whole Life Cycle

Abstract The vacuum-assisted resin infusion (VARI) technique provides considerable advantages in manufacturing large-scale composite structures. An accurate and consecutive structural health monitoring system is urgently required to determine the initial quality and assess the structural integrity of a composite structure. In this paper, a real-time active smart diagnostic system (SDS) based on piezoelectric sensor network is proposed to monitor the whole life-cycle of composite structures. Experiments were conducted on carbon fiber reinforced plastic (CFRP) specimens with different thicknesses to investigate the monitoring capability of piezoelectric lead-zirconate-titanate (PZT) sensors used in the SDS approach. The PZT sensor networks inserted inside the composite structures during the VARI process are used to monitor not only the curing parameters, but also the health status of composite structures when they are in service after curing. To monitor the curing process only, the sensor network can also be installed on the bottom of the mould. Experimental results demonstrate that both three-dimensional resin flow and degree of cure (DOC) in the VARI process can be effectively monitored by the PZT sensor network. Meanwhile, the embedded PZT sensor network has the potential to identify the different stages in the curing process. It is obvious that the piezoelectric sensor network will provide important technical support for composite materials with the structure and function integrated.

scholarly journals High-Performance Wireless Piezoelectric Sensor Network for Distributed Structural Health Monitoring

2016 ◽  
Vol 12 (3) ◽  
pp. 3846804 ◽  
Author(s):  
Shang Gao ◽  
Xuewu Dai ◽  
Zheng Liu ◽  
Guiyun Tian
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
High Performance ◽  
Piezoelectric Sensor ◽  
Structural Health

Monitoring of resin flow front and degree of cure in vacuum‐assisted resin infusion process using multifunctional piezoelectric sensor network

2020 ◽  
Author(s):  
Xiao Liu ◽  
Yukun Li ◽  
Jianjian Zhu ◽  
Yishou Wang ◽  
Xinlin Qing
Keyword(s):  
Sensor Network ◽  
Piezoelectric Sensor ◽  
Resin Flow ◽  
Flow Front ◽  
Resin Infusion ◽  
Degree Of Cure

Leaky Lamb wave–based resin impregnation monitoring with noninvasive and integrated piezoelectric sensor network

Measurement ◽  
2021 ◽  
pp. 110480
Author(s):  
Xiao Liu ◽  
Yinghong Yu ◽  
Jun Li ◽  
Jianjian Zhu ◽  
Yishou Wang ◽  
...  
Keyword(s):  
Sensor Network ◽  
Lamb Wave ◽  
Piezoelectric Sensor ◽  
Resin Impregnation ◽  
Leaky Lamb Wave

Computational Framework for Dense Sensor Network Evaluation Based on Model-Assisted Probability of Detection

2020 ◽  
Vol 78 (5) ◽  
pp. 573-583 ◽  
Author(s):  
Jin Yan ◽  
Simon Laflamme ◽  
Leifur Leifsson
Keyword(s):  
Sensor Network ◽  
Probability Of Detection ◽  
Computational Framework ◽  
Network Evaluation
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