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Sensing Technologies and Deep Learning Methods for Structural Health Monitoring...
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Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 1497

Special Issue Editors

Prof. Dr. Genda Chen

E-Mail Website1 Website2
Guest Editor
1. Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
2. Director of the Center for Intelligent Infrastructure, Missouri University of Science and Technology, Rolla, MO 65401, USA
3. Director of INSPIRE University Transportation Center, Missouri University of Science and Technology, Rolla, MO 65401, USA
4. Associate Director of Mid-America Transportation Center, University of Nebraska, Lincoln, NE 68588, USA
Interests: structural health monitoring; structural control; interface mechanics and deterioration; multihazard mitigation; bridge inspection and maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Woubishet Taffese

E-Mail Website
Guest Editor
Center for Intelligent Infrastructure, Missouri University of Science and Technology, Rolla, MO 65409, USA
Interests: predictive maintenance; digital twin; machine learning; deep learning; Internet of Things; computer vision

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue focused on the latest advancements at the intersection of deep learning and structural health monitoring (SHM). Sensors have become instrumental in SHM by providing crucial data for assessing the health and integrity of various structures. Recent innovations in deep learning methodologies present a promising avenue to enhance the analysis and interpretation of sensor-generated data for more accurate and efficient SHM. This Special Issue aims to compile state-of-the-art research exploring the integration of deep learning techniques with sensor data to advance structural health monitoring.

We invite researchers and practitioners to submit original research and review articles related to sensing technologies and deep learning methods for structural health monitoring systems. Topics of interest include, but are not limited to:

  • Deep learning algorithms for SHM;
  • Sensor fusion and multimodal data processing for comprehensive structural assessment;
  • Edge computing and real-time applications for SHM;
  • Transfer learning and domain adaptation for SHM tasks in various domains;
  • Uncertainty quantification and model interpretability in SHM systems.

Prof. Dr. Genda Chen
Dr. Woubishet Taffese
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • deep learning
  • sensor networks
  • intelligent monitoring techniques
  • computer vision
  • damage detection
  • Internet of Things

Published Papers (2 papers)

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Research

16 pages, 743 KiB  
Article
Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring
by Chengjie Huang, **njuan Sun and Yuxuan Zhang
Sensors 2024, 24(13), 4124; https://doi.org/10.3390/s24134124 - 25 Jun 2024
Viewed by 302
Abstract
The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of [...] Read more.
The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18 μJ of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security. Full article
(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)
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19 pages, 11574 KiB  
Article
Wind-Induced Vibration Monitoring of High-Mast Illumination Poles Using Wireless Smart Sensors
by Mona Shaheen, Jian Li, Caroline Bennett and William Collins
Sensors 2024, 24(8), 2506; https://doi.org/10.3390/s24082506 - 14 Apr 2024
Viewed by 680
Abstract
This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent dam**. Video recordings of multiple HMIPs showed considerable vibrations of these [...] Read more.
This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent dam**. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude displacements at the top, which can produce high-stress demand and lead to fatigue cracking at the bottom of the pole. In this study, the natural frequencies of the HMIP were assessed using pluck tests and finite element modeling, and the recorded vibration frequencies were obtained through computer vision-based video analysis. Meanwhile, a 30.48 m tall HMIP with three LED luminaires made of galvanized steel located in Wakeeney, Kansas, was selected for long-term vibration monitoring using wireless smart sensors to investigate the underlying mechanism for the excessive wind-induced vibrations. Data analysis with the long-term monitoring data indicates that while vortex-induced vibration occurs frequently at relatively low amplitude, buffeting-induced vibration was the leading cause of the excessive vibrations of the monitored HMIP. The findings provide crucial information to guide the design of vibration mitigation strategies for these HMIP structures. Full article
(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)
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