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Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring
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Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 3817

Special Issue Editors

Dr. Nizar Faisal Alkayem

E-Mail Website
Guest Editor
1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210046, China
2. College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: optimization; soft computing; structural health monitoring; damage detection; evolutionary computation; fuzzy logic; swarm algorithms; deep learning; manufacturing; welding; evolutionary algorithms; damage identification; neural networks; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maosen Cao

E-Mail Website
Guest Editor
Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; smart materials and structures; applied soft computing; structural vibration and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiang Wang

E-Mail Website
Guest Editor
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: structural health monitoring

Special Issue Information

Dear Colleagues,

A vast amount of infrastructure, such as bridges, roads, and buildings, has been constructed over the past several decades. However, the structural condition of this infrastructure can deteriorate over time due to long-term use, environmental factors, and loading from traffic/use. Small initial defects and damage can worsen and jeopardize the safety and integrity of these structures. As a result, there is expected to be a massive surge in demand for structural health monitoring (SHM) and predictive maintenance of in-service infrastructure over the next decade. Advancements in intelligent sensor networks, data acquisition systems, and the Internet of Things (IoT) have created new opportunities and shown great promise for SHM and predictive maintenance applications. Therefore, it is highly important to develop cutting-edge intelligent sensor technologies specifically for monitoring structural health and enabling the predictive maintenance of infrastructure. This Special Issue solicits research papers exploring the latest advances in intelligent sensor technologies for predictive maintenance and SHM of infrastructure. All submitted papers will undergo peer review before being published. Topics of interest include, but are not limited to, the following:

  • Intelligent sensor networks for SHM;
  • Wireless/online sensor networks for SHM data acquisition;
  • Internet of Things (IoT) for SHM sensor data collection;
  • Multi-sensor data fusion for SHM;
  • Embedded sensors for SHM of structural materials;
  • Energy-efficient sensor nodes for long-term SHM;
  • Sensor technologies for concrete crack detection;
  • Sensor technologies for structural condition assessment;
  • Sensor technologies for predictive maintenance;
  • Sensor data analytics for SHM and predictive maintenance;
  • Structural model updating using sensor data;
  • Deep learning for SHM sensor data analysis;
  • Machine learning for predictive maintenance using sensor data

Dr. Nizar Faisal Alkayem
Prof. Dr. Maosen Cao
Prof. Dr. Qiang Wang
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
  • intelligent sensors
  • wireless sensor networks
  • Internet of Things (IoT)
  • multi-sensor data fusion
  • structural model updating
  • deep learning
  • machine learning
  • concrete crack detection
  • structural condition assessment

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Published Papers (3 papers)

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Research

28 pages, 12307 KiB  
Article
Enhance the Concrete Crack Classification Based on a Novel Multi-Stage YOLOV10-ViT Framework
by Ali Mahmoud Mayya and Nizar Faisal Alkayem
Sensors 2024, 24(24), 8095; https://doi.org/10.3390/s24248095 - 19 Dec 2024
Viewed by 1388
Abstract
Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify [...] Read more.
Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify various concrete cracks. This study introduces a novel multi-stage deep learning framework for crack detection and type classification. First, the recently developed YOLOV10 model is trained to detect possible defective regions in concrete images. After that, a modified vision transformer (ViT) model is trained to classify concrete images into three main types: normal, simple cracks, and multi-branched cracks. The evaluation process includes feeding concrete test images into the trained YOLOV10 model, identifying the possible defect regions, and finally delivering the detected regions into the trained ViT model, which decides the appropriate crack type of those detected regions. Experiments are conducted using the individual ViT model and the proposed multi-stage framework. To improve the generation ability, multi-source datasets of concrete structures are used. For the classification part, a concrete crack dataset consisting of 12,000 images of three classes is utilized, while for the detection part, a dataset composed of various materials from historical buildings containing 1116 concrete images with their corresponding bounding boxes, is utilized. Results prove that the proposed multi-stage model accurately classifies crack types with 90.67% precision, 90.03% recall, and 90.34% F1-score. The results also show that the proposed model outperforms the individual classification model by 10.9%, 19.99%, and 19.2% for precision, recall, and F1-score, respectively. The proposed multi-stage YOLOV10-ViT model can be integrated into the construction systems which are based on crack materials to obtain early warning of possible future deformation in concrete structures. Full article
(This article belongs to the Special Issue Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring)
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21 pages, 19203 KiB  
Article
Design and Study of Pulsed Eddy Current Sensor for Detecting Surface Defects in Small-Diameter Bars
by Lei Han, Yi Jiang and Ming Yuan
Sensors 2024, 24(24), 8063; https://doi.org/10.3390/s24248063 - 18 Dec 2024
Viewed by 736
Abstract
The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection [...] Read more.
The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects. Full article
(This article belongs to the Special Issue Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring)
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20 pages, 11374 KiB  
Article
Investigation of Separating Temperature-Induced Structural Strain Using Improved Blind Source Separation (BSS) Technique
by Hao’an Gu, Xin Zhang, Dragoslav Sumarac, Jiayi Peng, László Dunai and Yufeng Zhang
Sensors 2024, 24(24), 8015; https://doi.org/10.3390/s24248015 - 15 Dec 2024
Viewed by 1008
Abstract
The strain data acquired from structural health monitoring (SHM) systems of large-span bridges are often contaminated by a mixture of temperature-induced and vehicle-induced strain components, thereby complicating the assessment of bridge health. Existing approaches for isolating temperature-induced strains predominantly rely on statistical temperature–strain [...] Read more.
The strain data acquired from structural health monitoring (SHM) systems of large-span bridges are often contaminated by a mixture of temperature-induced and vehicle-induced strain components, thereby complicating the assessment of bridge health. Existing approaches for isolating temperature-induced strains predominantly rely on statistical temperature–strain models, which can be significantly influenced by arbitrarily chosen parameters, thereby undermining the accuracy of the results. Additionally, signal processing techniques, including empirical mode decomposition (EMD) and others, frequently yield unstable outcomes when confronted with nonlinear strain signals. In response to these challenges, this study proposes a novel temperature-induced strain separation technique based on improved blind source separation (BSS), termed the Temperature-Separate Second-Order Blind Identification (TS-SOBI) method. Numerical verification using a finite element (FE) bridge model that considers both temperature loads and vehicle loads confirms the effectiveness of TS-SOBI in accurately separating temperature-induced strain components. Furthermore, real strain data from the SHM system of a long-span bridge are utilized to validate the application of TS-SOBI in practical engineering scenarios. By evaluating the remaining strain components after applying the TS-SOBI method, a clearer understanding of changes in the bridge’s loading conditions is achieved. The investigation of TS-SOBI introduces a novel perspective for mitigating temperature effects in SHM applications for bridges. Full article
(This article belongs to the Special Issue Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring)
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