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Infrastructures
Special Issues
Remote Sensing Techniques for Infrastructure Inspection and Monitoring
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Remote Sensing Techniques for Infrastructure Inspection and Monitoring

A special issue of Infrastructures (ISSN 2412-3811). This special issue belongs to the section "Infrastructures Inspection and Maintenance".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9460

Special Issue Editor

Dr. Linh Truong-Hong

E-Mail Website
Guest Editor
Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: building information modeling and digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental impact, excessive usage, overloading, and aging materials have an impact on reserve capacity, resilience, and the remaining service life and can even reduce the functionalities of infrastructure. A regular, rapid, cost-effective system to monitor and assess infrastructure structure is critical during regular operation and after extreme events (natural and human-made) for planning critical maintenance, repairs, and expansions. However, in practice, visual inspection with physical inspectors associated with equipment is still dominant when it comes to inspecting and assessing infrastructures (e.g., roads, bridges, or storage tanks), which may not detect changes and deficiencies of structure in a timely manner to prevent any consequent catastrophic collapse. Recently, several techniques based on satellite images, LiDAR, and photogrammetry have been implemented to acquire information for monitoring and inspection tasks. However, adopting these techniques to collect information around changes and different scales and types of damage (e.g., cracks or corrosion) of infrastructure with a high level of complexity—for example, materials, geometry, and orientation—is still a challenge. Moreover, robust, efficient methods are still required to automatically process such massive data acquired from those techniques to give reliable and accurate results for decision making.

This Special Issue solicits papers on the state of the art in developing remote sensing techniques for infrastructure inspection and monitoring. Topic will cover both methodologies in data collection and methods to process remote sensing data in an automated manner. The Special Issue also encourages practitioners to submit case studies on the implementation of remote sensing techniques for specific infrastructure.

Dr. Linh Truong-Hong
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. 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

  • satellite
  • InSAR
  • LiDAR
  • photogrammetry
  • artificial intelligence
  • drone
  • deformation
  • deficiency

Published Papers (5 papers)

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Research

18 pages, 8610 KiB  
Article
Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models
by Rafael Cabral, Rogério Oliveira, Diogo Ribeiro, Anna M. Rakoczy, Ricardo Santos, Miguel Azenha and José Correia
Infrastructures 2023, 8(7), 114; https://doi.org/10.3390/infrastructures8070114 - 20 Jul 2023
Cited by 1 | Viewed by 1345
Abstract
Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These [...] Read more.
Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These technologies can be used independently or in combination to provide a 3D as-is image-based model of the railway bridge. In this study, TLS captured the side and bottom sections of the deck, while the CRP-based UAV captured the side and top sections of the deck, and the track. The combination of post-processing techniques enabled the merging of TLS and CRP models, resulting in the creation of an accurate 3D representation of the complete railway bridge deck. Additionally, a 3D as-designed model was developed based on the design plans of the bridge. The as-designed model is compared to the as-is model through a 3D digital registration. The comparison allows the detection of dimensional deviation and surface alignments. The results reveal slight deviations in the structural dimension with a global average value of 9 mm. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Infrastructure Inspection and Monitoring)
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23 pages, 17370 KiB  
Article
SAR Interferometry Data Exploitation for Infrastructure Monitoring Using GIS Application
by Felipe Orellana, Peppe J. V. D’Aranno, Silvia Scifoni and Maria Marsella
Infrastructures 2023, 8(5), 94; https://doi.org/10.3390/infrastructures8050094 - 16 May 2023
Cited by 2 | Viewed by 1647
Abstract
Monitoring structural stability in urban areas and infrastructure networks is emerging as one of the dominant socio-economic issues for population security. The problem is accentuated by the age of the infrastructure because of increasing risks due to material deterioration and loss of load [...] Read more.
Monitoring structural stability in urban areas and infrastructure networks is emerging as one of the dominant socio-economic issues for population security. The problem is accentuated by the age of the infrastructure because of increasing risks due to material deterioration and loss of load capacity. In this case, SAR satellite data are crucial to identify and assess the deteriorating conditions of civil infrastructures. The large amount of data available from SAR satellite sensors leads to the exploitation and development of new GIS-based procedures for rapid responses and decision making. In recent decades, the DInSAR technique has been used efficiently for the monitoring of structures, providing measurement points located on structures with millimeter precision. Our study has analyzed the behavior of structures in settlements, attempting to discuss the interactions of soil and structures, and examining the behavior of different types of structures, such as roads and buildings. The method used is based on long-term SAR interferometry data and a semi-automatic procedure to measure the displacement (mm/year) of structures, through a GIS-based application performed in the “Implemented MOnitoring DIsplacement” I.MODI platform. The analysis provides extensive information on long-term spatial and temporal continuity of up to 25 years of record, using satellite SAR multi-sensors from ERS, Envisat, and COSMO-SkyMed. The interpretation uses time series spatial analysis, supported by orthophotos, and layers of the DBTR (regional topographic database), Digital Surface model (DSM), and hydrogeological map to show anomalous areas with a high displacement rate and to observe the correlation of settlements in the sediments. With the satellite information and Geographic Information System (GIS), we were able to observe relevant parameters, such as the velocity of advance in the direction of the slope (deformation profiles), the cumulative displacement, and the trend changes in structures. The results illustrate an innovative procedure that allows the management of DInSAR data to facilitate the effective management of structures in which a monitoring protocol was developed at different spatial scales, integrating the information into a GIS. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Infrastructure Inspection and Monitoring)
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Graphical abstract

13 pages, 11097 KiB  
Article
U-Net-Based CNN Architecture for Road Crack Segmentation
by Alessandro Di Benedetto, Margherita Fiani and Lucas Matias Gujski
Infrastructures 2023, 8(5), 90; https://doi.org/10.3390/infrastructures8050090 - 06 May 2023
Cited by 4 | Viewed by 1891
Abstract
Many studies on the semantic segmentation of cracks using the machine learning (ML) technique can be found in the relevant literature. To date, the results obtained are quite good, but often the accuracy of the trained model and the results obtained are evaluated [...] Read more.
Many studies on the semantic segmentation of cracks using the machine learning (ML) technique can be found in the relevant literature. To date, the results obtained are quite good, but often the accuracy of the trained model and the results obtained are evaluated using traditional metrics only, and in most cases, the goal is to detect only the occurrence of cracks. Particular attention should be paid to the thickness of the segmented crack since, in road pavement maintenance, the width of the crack is the main parameter and is the one that characterizes the severity levels. The aim of our study is to optimize the crack segmentation process through the implementation of a modified U-Net model-based algorithm. For this, the Crack500 dataset is used, and then the results are compared with those obtained from the U-Net algorithm, which is currently found to be the most accurate and performant in the literature. The results are promising and accurate, as the findings on the shape and width of the segmented cracks are very close to reality. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Infrastructure Inspection and Monitoring)
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26 pages, 7170 KiB  
Article
Performance Evaluation of Uncooled UAV Infrared Camera in Detecting Concrete Delamination
by Dyala Aljagoub, Ri Na, Chongsheng Cheng and Zhigang Shen
Infrastructures 2022, 7(12), 163; https://doi.org/10.3390/infrastructures7120163 - 30 Nov 2022
Cited by 1 | Viewed by 1838
Abstract
Concrete delamination detection using unmanned aerial vehicle (UAV)-mounted infrared cameras has proved effective in recent research. However, most studies used expensive research-grade infrared cameras and proprietary software to acquire images, which is hard to implement in state departments of transportation (DOTs) due to [...] Read more.
Concrete delamination detection using unmanned aerial vehicle (UAV)-mounted infrared cameras has proved effective in recent research. However, most studies used expensive research-grade infrared cameras and proprietary software to acquire images, which is hard to implement in state departments of transportation (DOTs) due to the lack of specialty professionals. Some state DOTs started deploying lightweight UAV-based consumer-grade infrared cameras for delamination detection. Quantitative performance evaluation of such a camera in concrete delamination detection is lacking. To fill this gap, this study intends to conduct a comprehensive assessment of the consumer-grade camera benchmarked against the results of a research-grade camera to see the practicality of using the small and low-cost camera in concrete delamination detection. Data was collected for a slab with mimicked delamination and two in-service bridge decks. For the case of the slab, maximum detectability of 70–72% was achieved. A transient numerical simulation was conducted to provide a supplemental and noise-free dataset to explore detectability accuracy peaks throughout the day. The results of the in-service bridge decks indicated that the consumer-grade infrared camera provided adequate detection of the locations of suspected delamination. Results of both the slab and in-service bridge decks were comparable to those of a research-grade infrared camera. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Infrastructure Inspection and Monitoring)
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12 pages, 3015 KiB  
Article
Development of Soundness Diagnostic Model for Concrete Slab Using Bridge Inspection Data
by Takahiro Minami, Tomotaka Fukuoka, Mai Yoshikura, Taiki Suwa and Makoto Fujiu
Infrastructures 2022, 7(6), 82; https://doi.org/10.3390/infrastructures7060082 - 08 Jun 2022
Viewed by 1842
Abstract
With the aging of bridges, the efficiency of periodic inspections has become a problem. As issues with the continuing close visual inspection of bridges are surfacing, remote imaging systems are expected to become a new inspection method that replaces close visual inspection. The [...] Read more.
With the aging of bridges, the efficiency of periodic inspections has become a problem. As issues with the continuing close visual inspection of bridges are surfacing, remote imaging systems are expected to become a new inspection method that replaces close visual inspection. The objective of the study is to develop a classification model of countermeasure categories using the results of past periodic inspections of bridges conducted by skilled inspectors. Focusing on concrete slabs, a model was constructed to classify the countermeasure categories based on the characteristics of the damage maps by random forest classification. As a result, it was possible to classify two classes of countermeasure categories with a macro-average precision rate of about 88%. It became clear that the degree of crack development and the number of cracks are the most important factors in the classification of judgment categories. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Infrastructure Inspection and Monitoring)
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