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Smart Sensors and Physics-Based Machine Learning for Structural Health Monitoring...
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Smart Sensors and Physics-Based Machine Learning for Structural Health Monitoring II

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

Deadline for manuscript submissions: closed (27 January 2023) | Viewed by 6164

Special Issue Editor

Dr. Enrique García Macías

E-Mail Website
Guest Editor
Department of Structural Mechanics and Hydraulic Engineering, University of Granada, 18001 Granada, Spain
Interests: multifunctional composite materials; multi-physics modeling; structural health monitoring; vibration-based testing; structural dynamics; structural damage identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in smart sensor systems and artificial intelligence (AI) have opened vast possibilities for the development of disruptive innovations in the field of structural health monitoring (SHM). In their broadest sense, smart sensors are designed to mitigate operating and efficiency limitations related to traditional monitoring solutions. These may range from sensors incorporating on-board microprocessing and state interrogation to sparse and dense sensor networks capable of detecting local and global pathologies, and novel composite materials with self-diagnostic properties. In addition, the increasingly frequent implementation of AI algorithms in the realm of SHM is enabling unprecedented possibilities to link monitoring signals to decision making. Particularly promising are physics-based AI applications, enabling the injection of engineering knowledge and expertise into decision-making processes. In this light, the aim of this Special Issue is to generate discussion on the latest advances in research on smart sensing technologies and physics-based AI for SHM. Topics of interest include but are not limited to:

  • Novel sensors and transducers;
  • Intelligent signal processing;
  • Smart sparse and dense sensor networks;
  • Integrated systems;
  • Multifunctional materials for sensing applications;
  • Data fusion;
  • Data mining;
  • Supervised/unsupervised machine learning;
  • Surrogate modeling for automated damage identification;
  • Long-term big data processing and management;
  • Internet of Things for structural health monitoring.

For the original issue, please find here: https://0-www-mdpi-com.brum.beds.ac.uk/journal/sensors/special_issues/ML_Structural_Health_Monitoring

Dr. Enrique García Macías
Guest Editor

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.

Published Papers (3 papers)

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Research

23 pages, 4128 KiB  
Article
Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring
by Shreyas Samudra, Mohamed Barbosh and Ayan Sadhu
Sensors 2023, 23(7), 3365; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073365 - 23 Mar 2023
Cited by 8 | Viewed by 2261
Abstract
The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring [...] Read more.
The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy. Full article
(This article belongs to the Special Issue Smart Sensors and Physics-Based Machine Learning for Structural Health Monitoring II)
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17 pages, 7345 KiB  
Article
Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering
by Jiayin Song, Jie Shi, Hongwei Zhou, Wenlong Song, Hongju Zhou and Yue Zhao
Sensors 2022, 22(24), 9840; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249840 - 14 Dec 2022
Viewed by 1017
Abstract
Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are [...] Read more.
Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images. Full article
(This article belongs to the Special Issue Smart Sensors and Physics-Based Machine Learning for Structural Health Monitoring II)
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21 pages, 5131 KiB  
Article
Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine Learning Classification
by Muflih Alhammad, Nicolas P. Avdelidis, Clemente Ibarra-Castanedo, Muhammet E. Torbali, Marc Genest, Hai Zhang, Argyrios Zolotas and Xavier P. V. Maldgue
Sensors 2022, 22(23), 9031; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239031 - 22 Nov 2022
Cited by 12 | Viewed by 2306
Abstract
Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts [...] Read more.
Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique. Full article
(This article belongs to the Special Issue Smart Sensors and Physics-Based Machine Learning for Structural Health Monitoring II)
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