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Sensor Technologies for Structural Health Monitoring and Monitoring Systems
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Sensor Technologies for Structural Health Monitoring and Monitoring Systems

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Physical Sensors".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1372

Editors

Dr. Diego Alexander Tibaduiza Burgos

E-Mail Website
Guest Editor
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Interests: structural health monitoring; pattern recognition; condition monitoring; sensors; digital design; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Maribel Anaya

E-Mail Website
Guest Editor
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Interests: structural health monitoring; signal processing strategies

Special Issue Information

Dear Colleagues,

Monitoring systems are indispensable tools for improving processes, reducing costs and preventing risks associated with accidents, quality incidents and hazardous events. These systems allow the process to move from reactive to proactive by providing relevant data to better understand the process and serving as a fundamental tool for decision-making. In addition, data is important to generate trends and predictions about the behaviour of variables in the process. The success of these systems lies in integrating new developments in sensors, processing capabilities, communication strategies and the use of artificial intelligence for the analysis of big data to provide the capabilities of a smart system.

Applications are under development across fields such as medicine, engineering and environmental science, among others. This Special Issue aims to present and disseminate the most recent advances in sensors, processing and methods for monitoring systems, smart technologies and IA applications. We consider contributions that address developments in this area, their applications and the impact of these systems in industry, research and education.

Topics of interest for publication include, but are not limited to, the following:

  • Anomaly detection
  • Sensor data fusion strategies
  • Predictive analysis
  • Advanced signal processing
  • Smart systems
  • Hardware and software developments and integration
  • Application of machine learning, deep learning and IA-based strategies
  • Digital Twins

Applications in structural health monitoring, environmental and ecosystems monitoring, healthcare and life sciences, smart cities and public infrastructure and industrial and manufacturing, among others, will be considered in this Special Issue.

Dr. Diego Alexander Tibaduiza Burgos
Dr. Maribel Anaya
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • sensors
  • monitoring systems
  • smart technologies
  • machine learning
  • deep learning
  • digital twins

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Published Papers (2 papers)

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Research

22 pages, 1274 KB  
Article
Training-Free Structural Damage Localization Using Spatial-Correlation Sensor Networks: Full-Scale Validation on a Seven-Story Reinforced-Concrete Building
by Esmaeil Ghorbani and Jürgen Hackl
Sensors 2026, 26(17), 5333; https://doi.org/10.3390/s26175333 - 23 Aug 2026
Viewed by 392
Abstract
Damage identification in instrumented structures is often framed through modal-parameter changes, finite element updating, or supervised classifiers. These approaches are powerful, but they require an explicit structural model, identified modes, labeled damage cases, or expert choices about reference sensors. This paper introduces a [...] Read more.
Damage identification in instrumented structures is often framed through modal-parameter changes, finite element updating, or supervised classifiers. These approaches are powerful, but they require an explicit structural model, identified modes, labeled damage cases, or expert choices about reference sensors. This paper introduces a new data-driven and training-free approach with limited physical priors, defining a sensor network where each sensor is a node and the edges are defined from the spatial correlation of sensor responses. The idea is to use each sensor time history as the measured structural dynamics feature while damage is localized from the edges, whose correlations change relative to a baseline. The method is demonstrated on a full-scale seven-story reinforced-concrete shear-wall building tested at UC San Diego, considering four progressive earthquake-induced damage states and one brace-modification state. The results are compared with those obtained from a previously published finite element model. The results reveal that this network-based approach localizes the damage states in agreement with previous studies with limited prior requirements and low computational cost. Beyond damage localization, this network representation provides sensor centrality, allowing informative sensors to be selected from data rather than chosen randomly or only from experimental intuitions. For the case study, using this sensor network, we find the most central sensors, those carrying the most information with reduced trial-and-error and reduced expert intervention, and use them to recover the first three natural frequencies as a secondary dynamic check. The results show that spatial correlation networks can screen for damage, localize affected regions, and guide modal parameter extraction without building an FE model. This study opens a research avenue in which network representations of multi-sensor structural dynamics complement traditional modal analysis for structural health monitoring, with dense or heterogeneous sensing systems. Full article
(This article belongs to the Special Issue Sensor Technologies for Structural Health Monitoring and Monitoring Systems)
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16 pages, 1364 KB  
Article
Benchmarking Multilayer Perceptron Configurations for Damage Classification in UAV Composite Wings Using Fiber Bragg Gratings Sensors
by David O. Briceño González, Julian Sierra-Perez, Maribel Anaya Vejar and Diego Tibaduiza Burgos
Sensors 2026, 26(11), 3377; https://doi.org/10.3390/s26113377 - 26 May 2026
Viewed by 590
Abstract
Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on [...] Read more.
Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications. Full article
(This article belongs to the Special Issue Sensor Technologies for Structural Health Monitoring and Monitoring Systems)
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