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Advanced Sensors in Nondestructive Testing and Structural Health Monitoring: 2nd Edition

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2082

Special Issue Editor

Dr. Miguel A. Machado

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Lisbon, Portugal
Interests: non-destructive testing; eddy currents; thermography; sensors; structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced sensors play a crucial role in enhancing the accuracy, reliability, and efficiency of Nondestructive Testing (NDT) and Structural Health Monitoring (SHM). Traditional methods are often time-consuming, subjective, and may be inadequate to detect hidden defects or early signs of damage. In contrast, advanced sensors enable real-time, remote, and nonintrusive monitoring, allowing for the early detection of structural issues and facilitating proactive maintenance and repair strategies. Furthermore, collecting and analyzing data from advanced sensors provides valuable insights into the structural health and performance of critical assets such as bridges, pipelines, aircraft, and buildings.

The scope of this Special Issue encompasses various technologies used in advanced sensors, including but not limited to optical fibers, piezoelectric materials, wireless sensor networks, ultrasound, electromagnetic waves, and smart materials. We invite submissions that explore the design, development, simulation, and validation of advanced sensors. Additionally, we encourage researchers to share their work on practical applications of advanced sensors in SHM, including case studies that demonstrate their effectiveness in real-world scenarios. Both reviews and original research articles are welcome, contributing to the advancement of knowledge and implementation in this field.

Topics include, but are not limited to, the following:

  • Design and simulation of advanced sensors;
  • Case studies and practical applications showcasing the use of advanced sensors in SHM;
  • Integration of advanced sensor technologies into existing NDT and SHM frameworks;
  • Data analysis and interpretation methods for extracting valuable insights from sensor data;
  • Proactive maintenance and repair strategies enabled by advanced sensor monitoring.

Dr. Miguel A. Machado
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 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-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

  • advanced sensors
  • nondestructive testing (NDT)
  • structural health monitoring (SHM)
  • sensor technologies
  • smart materials
  • numerical simulation
  • remote monitoring
  • proactive maintenance
  • structural integrity
  • real-time monitoring
  • wireless sensor networks
  • terahertz inspection
  • eddy currents
  • ultrasounds
  • thermography

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Related Special Issue

Published Papers (4 papers)

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Research

21 pages, 4154 KB  
Article
Automatic Modal Parameter Identification for Offshore Wind Turbines Using Modified Clustering-Based Methodology
by Yang Yang, Fayun Liang, Qingxin Zhu and Hao Zhang
Sensors 2026, 26(8), 2536; https://doi.org/10.3390/s26082536 - 20 Apr 2026
Viewed by 410
Abstract
Offshore wind power stands as a clean and low-carbon energy option that is booming as part of the efforts to achieve the goal of carbon neutrality. Effectively monitoring the dynamic response of wind turbines is a necessity to analyze the modal parameters, which [...] Read more.
Offshore wind power stands as a clean and low-carbon energy option that is booming as part of the efforts to achieve the goal of carbon neutrality. Effectively monitoring the dynamic response of wind turbines is a necessity to analyze the modal parameters, which are key parameters to assess whether the wind turbines are operating safely. Modal parameter identification for offshore wind turbines (OWTs) becomes essential through analyzing the dynamic response, given the limited acceptable range of natural frequencies under dynamic loads. This paper introduces a novel machine learning-based method that combines the SSI-data (data-driven stochastic subspace identification) modal parameter identification method with clustering analysis, employing DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the K-means cluster algorithm. The proposed method can automatically define the number of K-means clusters. The validation was carried out through a theoretical analysis using a four-degree-of-freedom model and Opensees numerical simulation model of an OWT. The verification and case study outcomes demonstrate that the proposed method possesses the accuracy required for automated modal parameter identification. Compared with the benchmark case results, the differences between the frequencies identified by the proposed method and the reference values are 0.0%, 0.30%, and 0.18% for the first three orders, respectively. This research not only provides valuable insights for professionals in related dynamic monitoring fields but also offers technical support for diagnosing abnormal states of OWTs utilizing dynamic response data. Full article
(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring: 2nd Edition)
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22 pages, 10772 KB  
Article
Non-Destructive Quantitative Characterization of Constituent Content in C/C–SiC Composites Based on Multispectral Photon-Counting X-Ray Detection
by Xin Yan, Kai He, Guilong Gao, Jie Zhang, Yuetong Zhao, Gang Wang, Yiheng Liu and Xinlong Chang
Sensors 2026, 26(8), 2331; https://doi.org/10.3390/s26082331 - 9 Apr 2026
Viewed by 369
Abstract
To enable non-destructive quantitative characterization of constituent content in C/C–SiC ceramic-matrix composites, this study develops a physics-guided framework based on multispectral photon-counting X-ray detection. In practical photon-counting measurements, multispectral attenuation features are jointly distorted by detector-response non-idealities, including charge sharing, K-escape, and finite [...] Read more.
To enable non-destructive quantitative characterization of constituent content in C/C–SiC ceramic-matrix composites, this study develops a physics-guided framework based on multispectral photon-counting X-ray detection. In practical photon-counting measurements, multispectral attenuation features are jointly distorted by detector-response non-idealities, including charge sharing, K-escape, and finite energy resolution, as well as by beam-hardening effects from the polychromatic X-ray source. To address this coupled problem, a Geant4 11.2-based detector-response model was incorporated into a unified correction workflow together with beam-hardening compensation, so that physically consistent multispectral attenuation vectors could be recovered for subsequent constituent inversion rather than merely for spectrum restoration. On this basis, a fine-grained theoretical database covering different SiC mass fractions was established, and quantitative constituent inversion was achieved by matching the corrected attenuation features to the database. Experimental results show that the proposed framework effectively suppresses thickness-dependent bias in attenuation measurements and yields an average relative error below 3% for pure aluminum. For C/C–SiC composites, the SiC mass fraction can be quantified with an accuracy better than 3 wt%. These results demonstrate that the proposed method provides a practical non-destructive route for constituent-content characterization in heterogeneous ceramic-matrix composites and is valuable for manufacturing quality control and in-service assessment. Full article
(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring: 2nd Edition)
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25 pages, 6266 KB  
Article
A Solution for Heritage Monitoring Based on Wireless Low-Cost Sensors and BIM: Application to the Monserrate Palace
by Rita Machete, Fábio M. Dias, Diogo M. Caetano, Ana Paula Falcão, Maria da Glória Gomes and Rita Bento
Sensors 2026, 26(7), 2015; https://doi.org/10.3390/s26072015 - 24 Mar 2026
Viewed by 529
Abstract
Conservation and management of built cultural heritage require multidisciplinary approaches and reliable information to support decision-making. In this context, digital transformation strategies that combine Building Information Modeling (BIM) with monitoring technologies offer significant potential to improve heritage management. This paper presents a monitoring [...] Read more.
Conservation and management of built cultural heritage require multidisciplinary approaches and reliable information to support decision-making. In this context, digital transformation strategies that combine Building Information Modeling (BIM) with monitoring technologies offer significant potential to improve heritage management. This paper presents a monitoring solution based on a wireless network of low-cost Internet of Things (IoT) sensors integrated within a Heritage Building Information Model (HBIM), applied to Monserrate Palace in Sintra, Portugal. The proposed approach covers all implementation stages, including HBIM development from as-built data collection, deployment of a wireless monitoring network for acceleration and environmental parameters, and integration of monitoring data into a BIM-based platform. The system aims to create a Digital Shadow of the building as a step towards a Digital Twin framework, enabling centralized visualization and management of structural and environmental information through the HBIM model and dedicated dashboards. Given the lower accuracy of low-cost sensors, in situ calibration with reference equipment was conducted to validate the recorded data. Implementing monitoring systems in heritage contexts presents challenges, such as limited historical documentation and the need for minimally invasive interventions. Despite these constraints, the proposed solution demonstrates the advantages of integrating monitoring data within HBIM, enabling centralized data management and improved understanding of building performance and conservation needs. Full article
(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring: 2nd Edition)
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18 pages, 5194 KB  
Article
Development of a Low-Cost Passive Strain Sensor for Bridge Structural Health Monitoring
by Hannah M. Power and Harry W. Shenton III
Sensors 2026, 26(6), 1963; https://doi.org/10.3390/s26061963 - 21 Mar 2026
Viewed by 324
Abstract
Complex structural health monitoring (SHM) systems are rarely installed on typical bridges, likely because of an expected low return on investment; however, low-cost, passive sensors made from a retroreflective sheeting material (RRSM) offer an economical alternative for SHM of typical bridges. Most departments [...] Read more.
Complex structural health monitoring (SHM) systems are rarely installed on typical bridges, likely because of an expected low return on investment; however, low-cost, passive sensors made from a retroreflective sheeting material (RRSM) offer an economical alternative for SHM of typical bridges. Most departments of transportation (DOTs) fabricate and maintain traffic signs made from RRSMs. By using a material familiar to DOTs, the technology transfer from signs to strain sensing is streamlined. This paper focuses on the development of a passive strain sensor made from an RRSM. A standard Type XI fluorescent yellow-green RRSM is tested in tension to establish the relationship between retroreflectivity (RR) and induced strain. Results show RR decreases linearly with increasing strain after an initial plateau of ~1000 × 10−6 m/m. To function as a strain sensor, the RRSM is pre-strained beyond the plateau. A production sensor is designed to attach to the tension face of a structural element for monitoring. Periodic RR measurements are used to estimate the likely maximum strain change at the sensor location. The sensor has the potential to provide a practical, low-cost, and easily implementable solution to improve the monitoring of typical bridges, enhancing their safety and longevity. Full article
(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring: 2nd Edition)
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