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Interferometric Sensors and Sensing Technologies for Structural Health Monitoring

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

Deadline for manuscript submissions: closed (25 November 2022) | Viewed by 4800

Special Issue Editors


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Guest Editor
Istituto Per Le Applicazioni Del Calcolo Mauro Picone, 00185 Rome, Italy
Interests: GNSS meteorology; (In)SAR meteorology; numerical weather prediction models; assimilation; ionosphere; VLF/LF; radio occultation
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Guest Editor
Department of Sciences and Technologies, University of Naples “Parthenope”, 80143 Naples, Italy

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) and damage identification of structural systems are essential to extend the working life of structures subjected to aging degradation, allowing the increase of safety and reliability and the optimization of proactive maintenance operations. Damage detection and continuous monitoring require advanced proper sensing techniques. In this context, an important role can be played by radar interferometry and optical fiber sensors. Radar interferometry provides a well-established microwave interferometric technique which combines coherent radar acquisition to extract the interferometric phase related to the displacements occurring in the monitored structure. On the other hand, optical fiber sensors, i.e., based on extrinsic Fabry–Perot interferometer (EFPI) or on fiber Bragg gratings (FBGs) sensors, are becoming more and more popular to monitor the local and global mechanical behavior of civil engineering infrastructures, allowing real time in situ measurements with the intrinsic advantages of optical fibers, such as immunity to electromagnetic interference, high sensitivity, reduced size, and multiplexibility. The aim of this Special Issue is to take a step forward, showing how information provided by interferometric sensors can integrate that of other sensing technologies and modeling techniques in SHM. The goal of this Special Issue is to gather high-quality original research articles and reviews on current research studying methods and data analysis involving the application of interferometric sensors to the monitoring of infrastructures (e.g., displacements, vibrations frequencies, accelerations). We seek new advances involving interferometric sensors applied to the SHM. We also welcome studies on sensing technologies (e.g., optical systems, total stations, GNSS, camera, laser scanning, LiDAR systems, and geotechnical sensors) and static and dynamic modeling techniques integrating measurements of interferometric sensors.

We are inviting submissions associated with the following applications:

Bridges;

Concrete and earth-filled dams;

Towers;

Wind towers;

Tunnels;

Harbor and airport infrastructures;

Road and railway infrastructures;

Retaining walls;

Modern and historical buildings;

Mines.

Dr. Giovanni Nico
Prof. Dr. Stefania Campopiano
Dr. Giuseppina Prezioso
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. 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

  • Structural health monitoring
  • Ground-based radar
  • Radar interferometry
  • Optical fiber

Published Papers (1 paper)

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Research

16 pages, 74019 KiB  
Article
Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data
by Ling Chang, Nikhil P. Sakpal, Sander Oude Elberink and Haoyu Wang
Sensors 2020, 20(24), 7108; https://doi.org/10.3390/s20247108 - 11 Dec 2020
Cited by 13 | Viewed by 3571
Abstract
Satellite radar interferometry (InSAR) techniques have been successfully applied for structural health monitoring of line-infrastructure such as railway. Limited by meter-level spatial resolution of Sentinel-1 satellite radar (SAR) imagery and meter-level geolocation precision, it is still challenging to (1) categorize radar scatterers (e.g., [...] Read more.
Satellite radar interferometry (InSAR) techniques have been successfully applied for structural health monitoring of line-infrastructure such as railway. Limited by meter-level spatial resolution of Sentinel-1 satellite radar (SAR) imagery and meter-level geolocation precision, it is still challenging to (1) categorize radar scatterers (e.g., persistent scatterers (PS)) and associate radar scatterers with actual objects along railways, and (2) identify unstable railway segments using InSAR Line of Sight (LOS) deformation time series from a single viewing geometry. In response to this, (1) we assess and improve the 3-D geolocation quality of Sentinel-1 derived PS using a 2-step method for PS 3-D geolocation improvement aided by laser scanning data; after geolocation improvement, we step-wisely classify railway infrastructure into rails, embankments and surroundings; (2) we recognize unstable rail segments by utilizing the (localized) differential settlement of rails in the normal direction (near vertical) which is yielded from the LOS deformation decomposition. We tested and evaluated the methods using 170 Sentinel-1a/b ascending data acquired between January 2017 and December 2019, over the Betuwe freight train track, in the Netherlands. The results show that 98% PS were associated with real objects with a significance level of 25%, the PS settlement measurements were generally in line with the in-situ track survey Rail Infrastructure aLignment Acquisition (RILA) measurements, and the standard deviations of the PS settlement measurements varied slightly with an average value of 6.16 mm. Full article
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