Three-Dimensional Acoustic Wavenumber Spectroscopy for Structural Health Monitoring

2019 ◽  
Author(s):  
PETER H. FICKENWIRTH ◽  
MATTHEW J. ADAMS ◽  
ERIC B. FLYNN
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Three Dimensional ◽  
Structural Health

Structural Health Monitoring (SHM) of Three-Dimensional Braided Composite Material using Carbon Nanotube Thread Sensors

2013 ◽  
Vol 29 (4) ◽  
pp. 617-621 ◽  
Author(s):  
Z. Wan ◽  
J. D. Li ◽  
M. Jia ◽  
J. L. Li
Keyword(s):  
Composite Material ◽  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Materials ◽  
Early Stage ◽  
Three Dimensional ◽  
Braided Composite ◽  
Structural Health ◽  
Smart Composite Materials

ABSTRACTStructural Health Monitoring (SHM) takes advantage of the recent advances in nanotechnology and sensing in order to monitor the behavior of a structure, assess its performance and identify damage at an early stage. Monitoring the state of strain throughout an entire structure is essential to determine its state of stress, detect potential residual stresses after fabrication, and also to help to establish its integrity. The Carbon nanotube thread was integrated into three-dimensional braiding materials and used for the first time as a sensor to monitor strain and also to detect damage in the three-dimensional braided composite material.In this paper a literature review about the application of carbon nanotubes thread for sensors and smart materials used for SHM of braiding structures is presented. The test data show the braided angle is important parameter for structural health monitoring of three-dimensional. The research will provide a new integrated and distributed technologies for the built-in carbon nanotube sensor to detect the health of composite. The subject will provide the new idea and method for the development of smart composite materials research and application.

Mechanic Models for Structure Health Monitoring Based on Wireless Sensors Networks Positioning

2014 ◽  
Vol 526 ◽  
pp. 93-98
Author(s):  
Wei Hua Fang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Complex Structure ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Structural Health ◽  
Mechanical Models ◽  
Complex Structural

In order to fully utilize the advantages of indoor 3-D positioning of wireless sensor networks, according to the needs of large, complex structural health monitoring, combined with fracture mechanics and continuum mechanics theory, corresponding mechanical models based on wireless sensor network positioning are provided. The models provide health diagnostic criterion for crack stability, as well as large and complex structure. The investigation lays the foundation for three-dimensional positioning technology for wireless sensor networks applications in structural health monitoring and has very important significance.

scholarly journals Highly sensitive frequency selective surface for structural health monitoring system

2019 ◽  
Vol 14 (2) ◽  
pp. 523
Author(s):  
S. A. Suhaimi ◽  
S. N. Azemi ◽  
P. J. Soh ◽  
C.B.M. Rashidi ◽  
A Abdullah Al-Hadi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Three Dimensional ◽  
Frequency Selective Surface ◽  
Structural Health ◽  
Ac Power ◽  
Highly Sensitive ◽  
Passive Sensor ◽  
Frequency Selective

<span>This paper is introduced a passive sensor to detect the performance of the structure using three-dimensional (3D) Frequency Selective Surfaces (FSS). The proposed 3D Circular FSS results are proved behave as passive sensor with changing of sensitivity incident angles to be apply in Structural Health Monitoring (SHM) system. Moreover, this 3D Circular FSS capable to operate without stand to any (DC/AC) power and very low cost in term of installation and maintenance.</span>

Efficient development of vision-based dense three-dimensional displacement measurement algorithms using physics-based graphics models

2020 ◽  
pp. 147592172093952
Author(s):  
Yasutaka Narazaki ◽  
Fernando Gomez ◽  
Vedhus Hoskere ◽  
Matthew D Smith ◽  
Billie F Spencer
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Displacement Measurement ◽  
Camera Motion ◽  
Two Dimensional ◽  
Civil Structures ◽  
Individual Measurement ◽  
Structural Health

This research investigates a framework for the efficient development of vision-based dense three-dimensional displacement measurement algorithms to support reliable structural health monitoring of civil structures. The framework exploits the use of a photo-realistic synthetic model, termed a physics-based graphics model, to simulate the entire process of vision-based measurement. At the same time, the synthetic environment is used to evaluate the performance of different post-processing algorithms quantitatively for a given measurement scenario, such as camera selection and camera placement. The effectiveness of the framework is demonstrated by optimizing the algorithms for the three-dimensional displacement measurement of a 14-bay laboratory truss structure. The vision-based dense three-dimensional displacement estimation algorithms optimized in this study consist of four steps: (1) camera parameter estimation, (2) camera motion estimation and compensation, (3) vision-based two-dimensional tracking, and (4) projection of two-dimensional tracking results to three-dimensional space. The algorithms use the knowledge from the finite element model to facilitate the implementation and maximize the measurement outcome, that is, model-informed approach. To test and evaluate the model-informed approach, synthetic videos are rendered for two measurement scenarios, that is, using a Digital Single Lens Reflex camera mounted on a tripod and using an Unmanned Aerial Vehicle camera. Then, the performance of the model-informed approach is evaluated by comparing the estimated displacement with the ground truth values. Based on the performance evaluation, an algorithm with the highest expected performance is selected for each of the two measurement scenarios. Finally, the selected algorithm is tested in a laboratory experiment. In contrast to the existing literature that investigates fixed individual measurement scenarios, the proposed framework can be used to test different measurement scenarios and estimate the outcome of each scenario before performing actual tests, facilitating the implementation of vision-based measurement for the structural health monitoring of civil structures.

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