Journal of Sensors

Sensor Networks for Structural Health Monitoring


Publishing date
01 Jan 2020
Status
Published
Submission deadline
23 Aug 2019

Lead Editor

1American University of Beirut, Beirut, Lebanon

2University of Adelaide, Adelaide, Australia

3Monash University, Clayton, Australia

4Institute of Fluid Flow Machinery, Polish Academy of Sciences, Gdansk, Poland


Sensor Networks for Structural Health Monitoring

Description

Structural Health Monitoring (SHM) systems provide an automated and continuous monitoring of aerospace, mechanical, and civil structures. This concept requires sensors to be embedded in or mounted on various components of the structure, to collect data in order to predict the current state and remaining life of the structure. Sensors can be clustered according to where they are placed and organized to provide information about different types of failure modes. The SHM systems may also have the ability to conduct an assessment and advise on methods of repair.

Many issues are associated with the implementation of SHM technology, such as sensor selection and placement on the structure, data communication and management, data analytics (feature extraction), and visualization (data fusion).

The objective of this Special Issue is to create a forum of discussion, for research scientists and engineers working in the area of SHM, advanced sensor technologies, sensor placement (ultrasonic transducers, vibrations sensors, etc.), and data analytics. The Special Issue also provides a platform to discuss the challenges associated with sensor integration and possible solutions to enhance its application in real structures. We invite researchers to submit both original research and review articles on any type of sensors towards applications of the SHM systems, innovative research and reviews.

Potential topics include but are not limited to the following:

  • Advanced sensor design and development for SHM
  • Wired and wireless sensing for SHM
  • Contact and noncontact sensors for flaw detection
  • Self-powered sensors for SHM applications
  • Sensor network design for continuous monitoring
  • Sensor placement strategies and optimization–failure assessment within the network for more robust designs
  • Sensor integration on structures and impact on the operational conditions
  • Feature extraction and data fusion of data collected from the sensor networks for damage detection and assessment

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 3060672
  • - Editorial

Sensor Networks for Structural Health Monitoring

Samir Mustapha | Ching-Tai Ng | ... | Pawel Malinowski
  • Special Issue
  • - Volume 2019
  • - Article ID 9614630
  • - Research Article

A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring

Rohan Soman | Pawel Malinowski
  • Special Issue
  • - Volume 2019
  • - Article ID 2607893
  • - Research Article

A Three-Dimensional Strain Rosette Sensor Based on Graphene Composite with Piezoresistive Effect

Zhiqiang Wu | Jun Wei | ... | Hao Chen
  • Special Issue
  • - Volume 2019
  • - Article ID 3140980
  • - Research Article

Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Liya Yu | Zheng Wang | Zhongjing Duan
  • Special Issue
  • - Volume 2019
  • - Article ID 5370838
  • - Research Article

Presenting a New Wireless Strain Method for Structural Monitoring: Experimental Validation

Amedeo Gregori | Emidio Di Giampaolo | ... | Chiara Castoro
  • Special Issue
  • - Volume 2019
  • - Article ID 4581672
  • - Research Article

A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

Xudong Qu | Jie Yang | Meng Chang
  • Special Issue
  • - Volume 2019
  • - Article ID 5494901
  • - Research Article

Optimal Uncalibrated RSS Indoor Positioning and Optimal Reference Node Placement Using Cramér-Rao Lower Bound

Xavier Tolza | Pascal Acco | ... | Manuel Bracq
  • Special Issue
  • - Volume 2019
  • - Article ID 3409525
  • - Research Article

Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges

Xudong Jian | Ye Xia | ... | Limin Sun
Journal of Sensors
 Journal metrics
See full report
Acceptance rate12%
Submission to final decision129 days
Acceptance to publication27 days
CiteScore2.600
Journal Citation Indicator0.440
Impact Factor1.9
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