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Infrastructures
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Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and...
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Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2719

Special Issue Editors

Dr. Guangwei Yang

E-Mail Website
Guest Editor
Virginia Tech Transportation Institute, Blacksburg, VA 24060, USA
Interests: deep learning; transportation safety; pavement design and management; 2D/3D imaging sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Guangyang Hou

E-Mail Website
Guest Editor
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA
Interests: infrastructure resilience; risk assessment; multiple hazards; transportation engineering; bridge engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Hang Zeng

E-Mail Website
Guest Editor
Department of Civil Engineering and Engineering Mechanics, University of Arizona, Tucson, AZ, USA
Interests: acoustic emission monitoring; nondestructive testing; sustainable infrastructure materials design
Dr. Guolong Wang

E-Mail Website
Guest Editor
Western Transportation Institute, Montana State University, Bozeman, MT 59717, USA
Interests: pavement management; machine learning; image analysis; pavement design

Special Issue Information

Dear Colleagues,

Ensuring the timely detection of structural damage is vital in terms of safeguarding the integrity and functionality of critical infrastructure across a diverse range of sectors, including aviation, roadways, railways, bridges, telecommunications, power and energy, water, waste management, and recreational facilities. Early identification of abnormal infrastructure conditions facilitates the swift issuance of warnings and the implementation of maintenance measures, averting potential loss of life, economic setbacks, and other adverse consequences. Moreover, ongoing monitoring provides valuable insights for efficient infrastructure management, enabling proactive maintenance planning and optimal resource allocation. However, the convergence of challenges, such as aging infrastructure, financial constraints, an increasing incidence of extreme weather events, and rapid climate change, underscores the urgent need for advanced sensor technologies and methodologies for comprehensive yet cost-effective structural health monitoring to enhance durability and extend service life. Fortunately, the emergence of transformative technologies in sensors, machine learning, the Internet of Things (IoT), edge computing, and artificial intelligence (AI) presents unparalleled opportunities. Leveraging these innovations for infrastructure health monitoring promises to revolutionize the sustainability and resilience of infrastructure through the development and implementation of cutting-edge tools and techniques, enabling more proactive maintenance, enhanced risk management, and improved overall infrastructure performance.

The aim of this Special Issue is to explore recent advancements in sensors and analysis techniques within the realm of infrastructure health monitoring. We particularly welcome multidisciplinary contributions and potential submission topics encompass, but are not restricted to:

  • The implementation of sensor networks for real-time monitoring of structural health parameters such as strain, vibration, and temperature;
  • The development of AI algorithms for predictive maintenance, leveraging sensor data to detect and forecast potential infrastructure failures;
  • The integration of IoT devices to enable remote monitoring and management of critical infrastructure assets;
  • The exploration of edge computing solutions to process sensor data locally and reduce latency in decision-making;
  • The application of machine learning techniques for anomaly detection and fault diagnosis in infrastructure systems;
  • The utilization of advanced image processing algorithms for visual inspection of infrastructure components using drones or cameras;
  • The investigation of wireless sensor networks for distributed monitoring of large-scale infrastructure networks;
  • Research into multi-modal data fusion techniques to combine information from various sensors and sources for comprehensive infrastructure health assessment.

Dr. Guangwei Yang
Dr. Guangyang Hou
Dr. Hang Zeng
Dr. Guolong 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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • remote sensing
  • infrastructure health monitoring
  • computer vision
  • artificial intelligence
  • deep learning
  • distress detection
  • edge computing
  • Internet of Things

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Published Papers (4 papers)

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Research

18 pages, 6030 KiB  
Article
Uncertainty Quantification to Assess the Generalisability of Automated Masonry Joint Segmentation Methods
by Jack M. W. Smith and Chrysothemis Paraskevopoulou
Infrastructures 2025, 10(4), 98; https://doi.org/10.3390/infrastructures10040098 - 18 Apr 2025
Viewed by 155
Abstract
Masonry-lined tunnels form a vital part of the world’s operational railway networks. However, in many cases their structural condition is deteriorating, so it is vital to undertake regular condition assessments to ensure their safety. In order to reduce costs and improve the repeatability [...] Read more.
Masonry-lined tunnels form a vital part of the world’s operational railway networks. However, in many cases their structural condition is deteriorating, so it is vital to undertake regular condition assessments to ensure their safety. In order to reduce costs and improve the repeatability of these assessments, automated deep learning-based tunnel analysis workflows have been proposed. However, for such methods to be applied in practice to a safety-critical situation, it is necessary to validate their conclusions. This study analysed how uncertainty quantification methods can be used to assess the test time performance of neural networks trained for masonry joint segmentation without the laborious labelling of additional ground truths. It applies test-time augmentation (TTA) and Monte Carlo dropout (MCD) to evaluate both the aleatoric and epistemic uncertainties of a selection of trained models. It then shows how these can be used to generate uncertainty maps to aid an engineer’s interpretation of the neural network output. Full article
(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)
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22 pages, 6875 KiB  
Article
Evaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learning
by Aditya Rahalkar, S. Morteza Mirzaei, Yang Chen, Carvel Holton and Mehdi Ahmadian
Infrastructures 2025, 10(4), 80; https://doi.org/10.3390/infrastructures10040080 - 31 Mar 2025
Viewed by 239
Abstract
This study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to [...] Read more.
This study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to evaluate the condition of the rail. This alternative approach uses a laser-based optical sensing system developed by the Railway Technologies Laboratory (RTL) located at Virginia Tech in Blacksburg, VA, combined with a machine learning calibration model. The optical sensing system can capture the fluorescence emitted by the grease to identify its presence, while the machine learning model classifies the extent of grease present into four thickness indices (TIs), from 0 to 3, representing heavy (3), medium (2), light (1) and low/no (0) lubrication. Both laboratory and field tests are conducted, with the results demonstrating the ability of the system to differentiate lubrication levels and measure the presence or absence of grease and TI with an accuracy of 90%. Full article
(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)
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20 pages, 9066 KiB  
Article
Evaluation of Performance of Repairs in Post-Tensioned Box Girder Bridge via Live Load Test and Acoustic Emission Monitoring
by Hang Zeng, Julie Ann Hartell and Robert Emerson
Infrastructures 2025, 10(3), 56; https://doi.org/10.3390/infrastructures10030056 - 9 Mar 2025
Viewed by 420
Abstract
In this paper, bridge live load testing was conducted to examine the performance of repairs on a section of a post-tensioned box girder bridge in Oklahoma City, Oklahoma. The live load test was performed with a single/group of truck(s) with known gross weight. [...] Read more.
In this paper, bridge live load testing was conducted to examine the performance of repairs on a section of a post-tensioned box girder bridge in Oklahoma City, Oklahoma. The live load test was performed with a single/group of truck(s) with known gross weight. The objective of this study was to characterize the behavior of the test bridge span by comparing the performance of a repair in situ as part of the bridge section’s structural response to that of a section known to be sound. To achieve the objective, the structural strain response was collected from several critical locations across the bridge girders. A comparative analysis of bridge behavior was carried out for the results from both the repaired and structurally sound areas to identify any deterioration and adverse changes. The structural strain response indicated an elastic behavior of the tested bridge span under three different load levels. Meanwhile, acoustic emission monitoring was implemented as a supplementary evaluation method. The acoustic emission intensity analysis also revealed an insignificant change in the effectiveness of the repair upon comparing results obtained from both locations. Although there were fluctuations in the b-value, it consistently remained above one across the different load testing scenarios, indicating no progressive damage and generally reflecting structural soundness, aligning with the absence of visible cracks in the monitored area. Full article
(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)
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27 pages, 4959 KiB  
Article
Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials
by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson and Mayorkinos Papaelias
Infrastructures 2025, 10(3), 51; https://doi.org/10.3390/infrastructures10030051 - 2 Mar 2025
Viewed by 904
Abstract
Structural health monitoring (SHM) in fiber-reinforced polymer (FRP) composites is essential to ensure safety and reliability during service, particularly in critical industries such as aerospace and wind energy. Traditional methods of analyzing Acoustic Emission (AE) signals in the time domain often fail to [...] Read more.
Structural health monitoring (SHM) in fiber-reinforced polymer (FRP) composites is essential to ensure safety and reliability during service, particularly in critical industries such as aerospace and wind energy. Traditional methods of analyzing Acoustic Emission (AE) signals in the time domain often fail to accurately detect subtle or early-stage damage, limiting their effectiveness. The present study introduces a novel approach that integrates frequency-domain analysis using the fast Fourier transform (FFT) with deep learning techniques for more accurate and proactive damage detection. AE signals are first transformed into the frequency domain, where significant frequency components are extracted and used as inputs to an autoencoder network. The autoencoder model reduces the dimensionality of the data while preserving essential features, enabling unsupervised clustering to identify distinct damage states. Temporal damage evolution is modeled using Markov chain analysis to provide insights into how damage progresses over time. The proposed method achieves a reconstruction error of 0.0017 and a high R-squared value of 0.95, indicating the autoencoder’s effectiveness in learning compact representations while minimizing information loss. Clustering results, with a silhouette score of 0.37, demonstrate well-separated clusters that correspond to different damage stages. Markov chain analysis captures the transitions between damage states, providing a predictive framework for assessing damage progression. These findings highlight the potential of the proposed approach for early damage detection and predictive maintenance, which significantly improves the effectiveness of AE-based SHM systems in reducing downtime and extending component lifespan. Full article
(This article belongs to the Special Issue Remote Sensing for Infrastructure Health Monitoring: Advancements in Sensors and Analysis)
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