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Dr. Manuel Cabaleiro

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Guest Editor

Applied Mechanics and Construction Department, Universidade de Vigo, Vigo, Spain
Interests: lidar; laser scanner; engineering; construction; photogrammetry

Dr. Borja Conde

E-Mail Website
Guest Editor

Department of Engineering Materials, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Vigo, Spain
Interests: application of geomatic technologies to the dimensional and structural monitoring of large infrastructure facilities; structural health monitoring using geotechnologies.

Special Issue Information

Dear Colleagues,

Monitoring the structural health of civil engineering and industrial infrastructure is crucial to both guarantee its safety and to plan for adequate maintenance measures. In this sense, nondestructive techniques, and, in particular, remote sensing technologies (Lidar, Photogrammetry, InfraRed Thermography, etc.), have seen widespread use in recent years. Furthermore, these techniques constitute a foundation for most 3D modeling approaches that carry out structural analysis functions based on numerical simulations or Building Information Modeling (BIM) and Heritage Building Information Modeling (HBIM) processes.

We are inviting authors to contribute to this Special Issue with the submission of original artilces covering any aspect of remote sensing and the application of nondestructive techniques in structural health monitoring and analysis in civil engineering and industrial facilities. Topics of interest include, but are not limited to:

Point cloud data processing for automatic detection, segmentation, and modeling of structural elements (algorithms and applications).
3D geometrical modeling for as-built BIM and HBIM used for structural health monitoring.
Detection and quantification of structural damage and defects based on remote sensing and nondestructive techniques.
Analysis of evolution of structural health over time using remote sensing and non-destructive techniques.
New methods and approaches for the development and calibration of FEM models based on sensing data.
Computational modeling for structural analysis purposes supported by remote sensing data.

Dr. Manuel Cabaleiro
Dr. Borja Conde
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. Remote Sensing 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 2700 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

Terrestrial Laser Scanner (TLS)
Infrared Thermography (IRT)
Nondestructive Techniques (NDT)
Laser Imaging Detection and Ranging (LiDAR)
point cloud data processing
3D modeling
Building Information Modeling (BIM)
Heritage Building Information Modeling (HBIM)
finite element method (FEM)
structural analysis

Published Papers (2 papers)

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Research

18 pages, 1626 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Response Measurement and Cable Tension Estimation Using an Unmanned Aerial Vehicle

by In-Ho Kim, Hyung-Jo Jung, Sungsik Yoon and Jong Woong Park

Remote Sens. 2023, 15(16), 4000; https://doi.org/10.3390/rs15164000 - 11 Aug 2023

Cited by 2 | Viewed by 1280

Abstract

Since all structures vibrate due to external loads, measuring and analyzing vibration data is a representative method of structural health monitoring. In this paper, we propose a non-contact cable estimation method using a vision sensor mounted on an unmanned aerial vehicle. A target cable among many cables can be identified through marker detection. In addition, the motion of the structure can be quickly captured using the extracted feature points. Although computer vision can be used to transform displacements of multiple axis, in this study, only the vertical displacement is considered to estimate tension. Finally, the cable tension can be estimated via the vibration method using the modal frequencies derived from the cable displacement. To verify the performance of the proposed method, lab-scale experiments were carried out and the results were compared with the conventional method based on the accelerometer. The proposed method showed a 3.54% error compared with the existing method and confirmed that the cable tension force can be estimated quickly at low cost. Full article

(This article belongs to the Special Issue Remote Sensing for Structural Health Monitoring and Structural Analysis in Civil Engineering and Industrial Facilities Structures)

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21 pages, 12096 KiB

Open AccessArticle

Deformation Detection of Mining Tunnel Based on Automatic Target Recognition

by Changqi Ji, Haili Sun, Ruofei Zhong, Mingze Sun, Jincheng Li and Yue Lu

Remote Sens. 2023, 15(2), 307; https://doi.org/10.3390/rs15020307 - 04 Jan 2023

Cited by 1 | Viewed by 1368

Abstract

Mining tunnels have irregular and diverse cross-sectional shapes. Structural deformation detection using mobile laser measurement has some problems, such as the inconvenient positioning of the deformation, difficulties in unifying the multiphase data, and difficulties in solving the section parameters. To address these problems, this paper proposes a mining tunnel deformation detection method based on automatic target recognition. Firstly, a mobile tunnel laser detection scheme combined with the target layout is designed. Secondly, a preview image of the tunnel lining is generated using the mobile laser point cloud data, and the index relationship between the image and point cloud is established. The target recognition accuracy of the You Only Look Once version 4 (YOLOv4) model is optimized by integrating the prediction confidence threshold, target spatial position, and target gray scale rule. Based on target recognition and positioning, the chord length and vault net height of the mining tunnel are calculated using gross error elimination and curve fitting. Finally, the engineering application of the model and algorithm is realized using ML.NET. The research method was verified using the field measurement data of the mining tunnel. The target recognition accuracy reached 100%, and the repeated deviations of the chord length and net height of the arch crown were 1.7 mm and 1.4 mm, respectively, which established the effectiveness and high accuracy of the research method. Full article

(This article belongs to the Special Issue Remote Sensing for Structural Health Monitoring and Structural Analysis in Civil Engineering and Industrial Facilities Structures)

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Remote Sensing for Structural Health Monitoring and Structural Analysis in Civil Engineering and Industrial Facilities Structures

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