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Structural Health Monitoring of Engineering Structures Using Smart Sensors
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Structural Health Monitoring of Engineering Structures Using Smart Sensors

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 3286

Special Issue Editor

Dr. Yee Yan Lim

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Southern Cross University, East Lismore, NSW 2480, Australia
Interests: piezoelectric; structural health monitoring; energy harvesting; concrete technology; structures and mechanics; fibre-reinforced polymer

Special Issue Information

Dear Colleagues,

The emergence of smart materials provides an alternative option in the field of structural health monitoring. Smart-materials-based monitoring techniques offer non-destructive and real-time monitoring capabilities to evaluate the health of a structure, including steel, aluminium, concrete, fibre and other engineering materials. Such techniques employ smart sensors which actively or passively interact with the structures upon installation. Any changes in the mechanical behaviour of the structures can be indirectly related to the signatures acquired from the smart sensors. 

This Special Issue aims to report high-quality theoretical, analytical and experimental investigations, including proof-of-concept, modelling and practical-oriented studies related to the smart-based structural health monitoring techniques. These include civil, mechanical, aerospace, electric and electronic, material, agriculture, biomedical and other systems.

Dr. Yee Yan Lim
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.

Keywords

  • smart materials
  • structural health monitoring
  • civil engineering
  • mechanical engineering
  • signal processing
  • modelling
  • experimental

Published Papers (1 paper)

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Research

33 pages, 16074 KiB  
Article
Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring
by Gilbert A. Angulo-Saucedo, Jersson X. Leon-Medina, Wilman Alonso Pineda-Muñoz, Miguel Angel Torres-Arredondo and Diego A. Tibaduiza
Sensors 2022, 22(4), 1484; https://doi.org/10.3390/s22041484 - 15 Feb 2022
Cited by 8 | Viewed by 2832
Abstract
Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in [...] Read more.
Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied. Full article
(This article belongs to the Special Issue Structural Health Monitoring of Engineering Structures Using Smart Sensors)
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