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Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing...
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Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3998

Special Issue Editors

Prof. Dr. Joan Ramon Casas Rius

E-Mail Website1 Website2
Guest Editor
Civil and Environmental Engineering, Universitat Politecnica de Catalunya (UPC-BarcelonaTECH), 08034 Barcelona, Spain
Interests: bridges; structural safety and reliability; structural health monitoring; dynamic testing; composite materials; inspection and maintenance; fiber optic sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Necati Catbas

E-Mail Website
Guest Editor
Civil Engineering, University of Central Florida, Orlando, FL, USA
Interests: civil infrastructure systems; bridges; structural identification; structural monitoring; modal analysis
Dr. Rolando A. Chacón

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: structural health monitoring; engineering education; plate buckling; ductility; instability; steel structures
Prof. Dr. Daniele Zonta

E-Mail Website
Guest Editor
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
Interests: civil engineering; structural health monitoring; smart structures; bridge management; Bayesian probability

Special Issue Information

Dear Colleagues,

Buildings are an important part of human society. Vital types of infrastructure, such as roads, buildings, high-speed railways, and bridges, are built all over the world. However, with the extension of running time and the increase in environmental load, these structures gradually lose stability, resulting in the degradation and slow failure of the structure. If this damage is not detected in time, it may threaten the normal operation of the structure, and even cause major harm. Therefore, as an important practical problem, structural health monitoring (SHM) has been paid increasing attention in various fields. Nondestructive technologies, especially remote sensing technologies (LiDAR, photogrammetry, infrared thermal imaging, etc.), provide technical support for the timely detection of safety hazards and ensure the safe operation of structures. In addition, these techniques form the basis of most 3D modeling methods that perform structural analysis functions based on numerical simulation or building information modeling (BIM) and heritage building information modeling (HBIM) processes.

In this context, the second edition of this Special Issue aims to include state-of-the-art research, discuss advanced remote sensing techniques and data processing methods that can be used for structural damage mapping and resilience assessment, present some of the most relevant research currently being conducted, highlight future challenges, and include several case studies.

Topics of interest will include, but are not limited to, the following:

  • Damage identification and maintenance of modern and historic buildings;
  • Structural deformation monitoring and analysis by time-series InSAR;
  • Structural damage mapping by Lidar;
  • Structural reconstruction by remote sensing;
  • Remote sensing data processing;
  • Multisource remote sensing fusion method and application;
  • Structural damage identification based on deep learning;
  • Structural resilience assessment based on damage mapping.

Prof. Dr. Joan Ramon Casas Rius
Prof. Dr. Necati Catbas
Dr. Rolando A. Chacón
Prof. Dr. Daniele Zonta
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

  • structural health monitoring
  • remote sensing
  • deformation monitoring
  • time-series InSAR
  • lidar
  • damage identification
  • deep learning
  • resilience assessment

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Related Special Issue

Published Papers (5 papers)

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Research

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22 pages, 8045 KiB  
Article
A GIS Plugin for the Assessment of Deformations in Existing Bridge Portfolios via MTInSAR Data
by Mirko Calò, Sergio Ruggieri, Andrea Nettis and Giuseppina Uva
Remote Sens. 2024, 16(22), 4293; https://doi.org/10.3390/rs16224293 - 18 Nov 2024
Viewed by 300
Abstract
The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining [...] Read more.
The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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18 pages, 6544 KiB  
Article
Remote Inspection of Bridges with the Integration of Scanning Total Station and Unmanned Aerial Vehicle Data
by Piotr Olaszek, Edgar Maciejewski, Anna Rakoczy, Rafael Cabral, Ricardo Santos and Diogo Ribeiro
Remote Sens. 2024, 16(22), 4176; https://doi.org/10.3390/rs16224176 - 8 Nov 2024
Viewed by 631
Abstract
Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and [...] Read more.
Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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Graphical abstract

29 pages, 17710 KiB  
Article
Trans-DCN: A High-Efficiency and Adaptive Deep Network for Bridge Cable Surface Defect Segmentation
by Zhihai Huang, Bo Guo, Xiaolong Deng, Wenchao Guo and Xing Min
Remote Sens. 2024, 16(15), 2711; https://doi.org/10.3390/rs16152711 - 24 Jul 2024
Viewed by 796
Abstract
Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven [...] Read more.
Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven lighting and difficulties in addressing multiscale defect features. To address these challenges, this paper proposes a novel and cost-effective deep learning segmentation network, named Trans-DCN, to detect defects in the surface of the bridge cable. The network leverages an efficient Transformer-based encoder and integrates multiscale features to overcome the limitations associated with local feature inadequacy. The decoder implements an atrous Deformable Convolution (DCN) pyramid and dynamically fuses low-level feature information to perceive the complex distribution of defects. The effectiveness of Trans-DCN is evaluated by comparing it with state-of-the-art segmentation baseline models using a dataset comprising cable bridge defect images. Experimental results demonstrate that our network outperforms the state-of-the-art network SegFormer, achieving a 27.1% reduction in GFLOPs, a 1.2% increase in mean Intersection over Union, and a 1.5% increase in the F1 score. Ablation experiments confirmed the effectiveness of each module within our network, further substantiating the significant validity and advantages of Trans-DCN in the task of bridge cable defect segmentation. The network proposed in this paper provides an effective solution for downstream cable bridge image analysis. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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15 pages, 9673 KiB  
Article
Measurement Refinements of Ground-Based Radar Interferometry in Bridge Load Test Monitoring: Comprehensive Analysis on a Multi-Span Cable-Stayed Bridge
by Yaowen Chen, Qihuan Huang, Tingbin Zhang, Ming Zhou and Liming Jiang
Remote Sens. 2024, 16(11), 1882; https://doi.org/10.3390/rs16111882 - 24 May 2024
Viewed by 734
Abstract
This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) [...] Read more.
This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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Other

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13 pages, 3880 KiB  
Technical Note
Experimental Co-Polarimetric GPR Survey on Artificial Vertical Concrete Cracks by the Improved Time-Varying Centroid Frequency Scheme
by Xuebing Zhang, Junxuan Pei, Xianda Sha, Xuan Feng, Xin Hu, Changle Chen and Zhengchun Song
Remote Sens. 2024, 16(12), 2095; https://doi.org/10.3390/rs16122095 - 10 Jun 2024
Cited by 1 | Viewed by 908
Abstract
The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe [...] Read more.
The experimental setup is devised to simulate the presence of vertical cracks with varying widths within concrete structures. Co-polarimetric ground-penetrating radar (GPR) surveys are carried out to acquire the “VV” and “HH” polarization data. The time-varying centroid frequency attribute is employed to describe the vertical variation in the center frequency of the radar wave, unveiling a gradual vertical decay in the centroid frequency at the locations of vertical cracks. An improved time-varying centroid frequency attribute based on the adaptive sparse S-transform (ASST) is proposed and tested by a finite-difference time-domain model and co-polarimetric GPR data, which can offer better resolution compared to that of the conventional S-transform. By analyzing the waveform and centroid frequency properties of the two polarizations, we conclude that the “VV” polarization is relatively sensitive to centimeter scale cracks, while the “HH” polarization is more sensitive to millimeter scale cracks. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)
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