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Applied Sciences
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Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition
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Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 2672

Special Issue Editors

Prof. Dr. Grzegorz Psuj

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Guest Editor
Center for Electromagnetic Fields Engineering and High-Frequency Techniques, Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland
Interests: electromagnetic non-destructive testing and evaluation; magnetic sensors; electromagnetic field measurements; multi-sensor measuring systems; sensors network; multi-source data mining and fusion; data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Barbara Grochowalska (Szymanik)

E-Mail Website
Guest Editor
Center for Electromagnetic Fields Engineering and High-Frequency Techniques, Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland
Interests: active infrared thermography; microwave heating; numerical modelling; optimisation; neural networks; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the positive reception of our previous Special Issue entitled "Structural Health Monitoring: Latest Applications and Data Analysis," we are delighted to present its continuation in the form of a second edition. Similar to the previous edition, this particular Special Issue is dedicated to the topic of Structural Health Monitoring (SHM), with a specific focus on its latest applications and corresponding data analysis. The surveillance of engineering structures is undoubtedly a key matter; however, it encompasses a vast and multidisciplinary scope. In this context, both novel sensor systems and techniques for measurement, data collection, and processing are relevant. Structural Health Monitoring (SHM) techniques comprise a range of technical diagnostic and nondestructive testing methods that are widely utilized. These methods include electromagnetic, high frequency, thermovision, radiography, ultrasound, and various others. Each of the aforementioned techniques poses distinctive challenges, not only in relation to the methodology of measurement but also with regard to data processing. Consequently, the processing of signals or images for the purpose of qualitative and quantitative evaluation of the structures being examined is of particular interest in this context.

Researchers in the broad domain of technical diagnostics and nondestructive testing are cordially invited to present their innovative contributions pertaining to the above-mentioned topics. All types of research are welcome, including theoretical and experimental studies, as well as comprehensive reviews and surveys.

Prof. Dr. Grzegorz Psuj
Dr. Barbara Grochowalska (Szymanik)
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. Applied Sciences 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 2400 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
  • electromagnetic nondestructive evaluation
  • technical diagnostics
  • magnetic methods
  • thermovision
  • high-frequency electromagnetic methods
  • ultrasound methods
  • radiography
  • multi-sensor measuring systems
  • sensors network
  • multi-source data mining and fusion
  • data processing
  • image processing
  • numerical modelling
  • optimisation
  • neural networks
  • machine vision
  • machine learning

Related Special Issue

Published Papers (3 papers)

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Research

18 pages, 12799 KiB  
Article
The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
by Long Li, Zhiyuan Liu, Hengyi Zhao, Lin Xue and Jianbo Wu
Appl. Sci. 2024, 14(5), 1747; https://doi.org/10.3390/app14051747 - 21 Feb 2024
Viewed by 459
Abstract
As a critical foundational component, bearings find widespread application in various mechanical equipment. In order to achieve automated defect detection in the bearing-manufacturing process, a defect detection algorithm combining magnetic particle inspection with deep learning is proposed. Dynamic thresholding and generative adversarial network [...] Read more.
As a critical foundational component, bearings find widespread application in various mechanical equipment. In order to achieve automated defect detection in the bearing-manufacturing process, a defect detection algorithm combining magnetic particle inspection with deep learning is proposed. Dynamic thresholding and generative adversarial network (GAN) methods are employed to extract defect samples from bearing images and augment the dataset, thereby enhancing data diversity. To mitigate the impact of irrelevant displays in bearing images, a coordinated attention (CA) mechanism is introduced into the backbone network of the deep learning model to focus on key information. Additionally, an adaptive spatial feature fusion module (ASFF) is incorporated during the multiscale fusion stage to maintain consistency in features across different hierarchical levels. The weighted intersection over union (WIoU) bounding box loss function is utilized to replace the original generalized intersection over union (GIoU) in the network, directing the model’s attention towards common-quality anchor boxes to reduce the adverse effects of inconsistent annotations. The experimental results demonstrate that the improved network achieves a mean average precision (mAP) of 98.4% on the bearing dataset, representing a 4.2% improvement over the original network. Full article
(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition)
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14 pages, 7922 KiB  
Article
Study of the Kinetics of Adhesive Bond Formation Using the Ultrasonic Method
by Jakub Kowalczyk, Marian Jósko, Daniel Wieczorek, Kamil Sędłak and Michał Nowak
Appl. Sci. 2024, 14(1), 163; https://doi.org/10.3390/app14010163 - 24 Dec 2023
Viewed by 543
Abstract
Adhesive bonding is widely used in modern industry. It has many advantages—the main one being the reduction in production costs. It also has certain limitations. One of the limitations of adhesive bonds is the relatively long bonding time of the joints. The main [...] Read more.
Adhesive bonding is widely used in modern industry. It has many advantages—the main one being the reduction in production costs. It also has certain limitations. One of the limitations of adhesive bonds is the relatively long bonding time of the joints. The main objective of this research was to determine the possibility of studying the kinetics of adhesive bond formation using a non-destructive ultrasonic method. A research experiment was planned and carried out. Adhesive specimens were prepared, and their quality changes over time were evaluated. In addition, the change in ultrasonic measures during the testing of these bonds was evaluated, as well as the hardness of the adhesive. In this study, the choice of test apparatus was made, in particular ultrasonic probes for the adhesive used and the materials to be bonded. The choice of adhesive was also made, for one in which bonding phenomena occur uniformly throughout the volume. This work examined the changes in the mechanical strength and hardness with time. The tests showed that the greatest changes in mechanical strength occur within the first 24 h after the bond was made. With the mechanical strength reaching 12.6 Mpa after 216 h, the strength in the first 24 h was 10.36 (for bonded steel sheets). For bonded steel discs, the maximum tensile strength was 26.99 Mpa (after 216 h), with a hardness of 22.93 Mpa during the first 24 h. Also, significant changes were observed in the adhesive hardness during the first 24 h. The hardness of the adhesive after 216 h was 70.4 Shore’a on the D scale, while after 24 h it was 69.4 Shore’a on the D scale. Changes in the ultrasonic parameters of the adhesive bond quality were found to occur along with changes in the bond quality. Full article
(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition)
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17 pages, 9910 KiB  
Article
Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach
by Sam Ang Keo, Barbara Szymanik, Claire Le Roy, Franck Brachelet and Didier Defer
Appl. Sci. 2023, 13(14), 8393; https://doi.org/10.3390/app13148393 - 20 Jul 2023
Cited by 6 | Viewed by 1253
Abstract
This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal [...] Read more.
This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP. Full article
(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition)
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