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Advanced Deep Learning and Sensing Techniques for Complex Structural Health...
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Advanced Deep Learning and Sensing Techniques for Complex Structural Health Monitoring

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 13877

Special Issue Editors

Dr. Subhendu Kumar Pani

E-Mail Website
Guest Editor
Krupajal Engineering College, BPUT, Odisha 752104, India
Interests: data mining; big data analysis; web data analytics; fuzzy decision making; computational intelligence
Dr. Kewei Sha

E-Mail Website
Guest Editor
Department of Computing Sciences, University of Houston-Clear Lake, Houston, TX 77058, USA
Interests: Internet of Things; cyber-physical systems; edge computing; security and privacy
Dr. Vasile Palade

E-Mail Website
Guest Editor
Faculty of Engineering and Computing, Coventry University, Coventry CV1 2JH, UK
Interests: neural networks and deep learning; neuro-fuzzy systems; nature inspired algorithms
Dr. Brik Bouziane

E-Mail Website
Guest Editor
DRIVE Laboratory, Université Bourgogne Franche-Comté, Besancon 25030 CEDEX, France
Interests: vehicular and sensor networks; machine learning; deep learning; smart grids

Special Issue Information

Dear Colleagues,

In recent years, sensing, together with deep learning techniques, for complex structural health monitoring (SHM) has gained special consideration from both academia and industry. Sensing and deep learning are two influential and effective methods for data-based SHM. Additionally, the application of next-generation sensors aids researchers and engineers in the use of advanced image, signal, and video processing techniques‌, and in reaping the benefits of the Internet of Things (IoT) paradigm for the health monitoring and condition assessment of civil, mechanical, and aerospace systems.

SHM algorithms are applied to extract information from massive data and to make decisions about structural conditions. The fast progress of advanced sensing technologies will overcome the issues with the realization of smart systems and structures.

The aim of this Special Issue is to highlight the latest research on data-based SHM with sensing and machine learning techniques.

Potential topics include, but are not limited to, the following:

  • SHM data mining, processing, modeling, analysis, and condition assessment using deep learning techniques;
  • Sensor data-driven structural damage assessments;
  • Deep learning-based structural response prediction;
  • Sensor array-based multi-modal sensing principles;
  • Application of novel signal, image, and video processing methods to SHM;
  • Deep learning and intelligent monitoring techniques for SHM;
  • Deep learning-based coverage path planning for SHM;
  • Deep learning-based structural health monitoring from dense sensor networks;
  • Deep learning for construction equipment activity recognition;
  • Signal processing approach to structural health monitoring in noisy environments.

Dr. Subhendu Kumar Pani
Dr. Kewei Sha
Dr. Vasile Palade
Dr. Brik Bouziane
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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
  • data mining
  • signal processing
  • image processing
  • deep learning
  • intelligent monitoring techniques
  • sensing techniques
  • sensor networks

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Published Papers (3 papers)

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Research

13 pages, 4205 KiB  
Article
Bolt Positioning Detection Based on Improved YOLOv5 for Bridge Structural Health Monitoring
by Diyong Wang, Meixia Zhang, Danjie Sheng and Weiming Chen
Sensors 2023, 23(1), 396; https://doi.org/10.3390/s23010396 - 30 Dec 2022
Cited by 9 | Viewed by 2639
Abstract
To improve the stability of the bridge structure, we detect bolts in the bridge which cause the symmetry failure of the bridge center. For data acquisition, bolts are small-scale objects under complex background in images, and their feature expression ability is limited. Due [...] Read more.
To improve the stability of the bridge structure, we detect bolts in the bridge which cause the symmetry failure of the bridge center. For data acquisition, bolts are small-scale objects under complex background in images, and their feature expression ability is limited. Due to those questions, we propose a new bolt positioning detection based on improved YOLOv5 for bridge structural health monitoring. This paper makes three major contributions. Firstly, according to the calibration anchor boxes of bolts, the size and proportion parameters of the initial anchor boxes are optimized by K-means++ clustering algorithm to solve the initial clustering problem of anchor boxes in object detection. Second, the hypercolumn (HC) technique fuses the low-level global features of the trunk and the high-level local features of three different scales to solve the problem of the inefficient distribution of anchors and insufficient extraction of classification features. In this way, we improve the detection accuracy and speed of bolt detection. Finally, we establish a dataset of bridge bolts through network collection and public datasets, including 1494 images. We compare and verify the new method in the collected bolt dataset. The experimental results show that the precision (P) of the improved YOLOv5x is up to 87.3%, and the average precision (AP) is up to 86.3%, which are 6.5% and 5.9% higher than the original YOLOv5x, respectively. Full article
(This article belongs to the Special Issue Advanced Deep Learning and Sensing Techniques for Complex Structural Health Monitoring)
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26 pages, 7848 KiB  
Article
A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring
by Lukesh Parida, Sumedha Moharana, Victor M. Ferreira, Sourav Kumar Giri and Guilherme Ascensão
Sensors 2022, 22(24), 9920; https://doi.org/10.3390/s22249920 - 16 Dec 2022
Cited by 30 | Viewed by 5800
Abstract
The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel [...] Read more.
The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel monitoring techniques for structural monitoring and evaluation. This study aims to determine the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test. The concrete cylindrical samples with embedded steel bars were prepared, cured for 28 days, and a pull-out test was performed to measure the interfacial bond between them. The piezo coupled signatures were obtained for the PZT patch bonded to the steel bar. The damage qualification is performed through the statistical indices, i.e., root-mean-square deviation (RMSD) and correlation coefficient deviation metric (CCDM), were obtained for different displacements recorded for axial pull. Furthermore, this study utilizes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based hybrid model, an effective regression model to predict the EMI signatures. These results emphasize the efficiency and potential application of the deep learning-based hybrid model in predicting EMI-based structural signatures. The findings of this study have several implications for structural health diagnosis using a deep learning-based model for monitoring and conservation of building heritage. Full article
(This article belongs to the Special Issue Advanced Deep Learning and Sensing Techniques for Complex Structural Health Monitoring)
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19 pages, 2729 KiB  
Article
Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification
by Abdulaziz Alshammari
Sensors 2022, 22(20), 8076; https://doi.org/10.3390/s22208076 - 21 Oct 2022
Cited by 24 | Viewed by 3786
Abstract
Brain metastases (BMs) happen often in patients with metastatic cancer (MC), requiring initial and precise diagnosis of BMs, which remains important for medical care preparation and radiotherapy prognostication. Nevertheless, the susceptibility of automated BM (ABMS) diagnosis is unfairly great for minute BMs, and [...] Read more.
Brain metastases (BMs) happen often in patients with metastatic cancer (MC), requiring initial and precise diagnosis of BMs, which remains important for medical care preparation and radiotherapy prognostication. Nevertheless, the susceptibility of automated BM (ABMS) diagnosis is unfairly great for minute BMs, and integrating into medical exercises to distinguish true metastases (MtS) from false positives remains difficult. For enhancing BM classification execution, MtS localization is performed through the NestNet framework. Subsequent to segmentation, classification is performed by employing the VGG16 convolution neural network. A novel loss function is computed by employing the weighted softmax function (WSF) for enhancing minute MtS diagnosis and for calibrating susceptibility and particularity. The aim of this study was to merge temporal prior data for ABMS detection. The proffered VGG16_CNN is capable of differentiating positive MtS among MtS candidates with high confidence, which typically needs distinct specialist analysis or additional investigation, remaining specifically apt for specialist reinforcement in actual medical practice. The proffered VGG16_CNN framework can be correlated with three advanced methodologies (moU-Net, DSNet, and U-Net) concerning diverse criteria. It was observed that the proffered VGG16_CNN attained 93.74% accuracy, 92% precision, 92.1% recall, and 67.08% F1-score. Full article
(This article belongs to the Special Issue Advanced Deep Learning and Sensing Techniques for Complex Structural Health Monitoring)
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