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Remote Sensing-Based Structural Health Monitoring and Damage Assessment

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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 25877

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Special Issue Editors

Dr. Xiaoqiong Qin

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College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: time-series InSAR; land subsidence monitoring; structural health monitoring
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Dr. Ling Chang

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Guest Editor

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514AE Enschede, The Netherlands
Interests: geodetic analysis of imaging remote sensing data and data integration; deformation time series modeling and statistical hypothesis testing; physical interpretation of deformation processes
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Dr. Jie Dong

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: early identification and monitoring of geological hazards; satellite-based/ground-based InSAR algorithms
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Dr. Xuguo Shi

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data remote sensing for landslide deformation monitoring; geological hazard monitoring; radar interferometry
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Prof. Dr. Joan Ramon Casas Rius

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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
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Dr. Jónatas Valença

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CERIS, Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, n.1, 1049-001 Lisbon, Portugal
Interests: inspection and diagnosis of built heritage; structural health monitoring; digital construction
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Special Issue Information

Dear Colleagues,

With the rapid development of society and the economy, significant infrastructure such as roads, buildings, high-speed railways, and bridges have been built all over the world. However, the increase in operating time and environmental loads have destabilized the structures, resulting in slow structural damage. Such damage, if not detected in time, can threaten normal structural operations or even cause significant hazards. Therefore, the operational safety of urban infrastructures, as an important practical issue, has attracted increasing attention from multi-disciplinary fields such as public security, earth observation, civil engineering, and so on. However, since the urban infrastructure is widely distributed, the current manual periodic detection and on-site automatic sensor monitoring methods are spatially or temporally incomplete and damage could remain undetected. Thus, there is an urgent need to develop scientific and efficient technical methods to carry out convenient and accurate structural damage monitoring, providing technical support for the timely detection of potential safety hazards and ensuring their safe operation.

Remote sensing technology can quickly obtain large-scale surface information, which outperforms the traditional methods with high measurement accuracy, non-destructiveness, continuous space coverage, and so on. Benefiting from the rapid development of remote sensing techniques (higher resolution, shorter revisit time, more bands and platforms, etc.), research on these techniques has been very active in the past few decades. In this context, this Special Issue of “Remote Sensing-based Structural Damage Mapping” aims to include state-of-the-art studies that discuss the remote sensing techniques available for structural damage mapping and resilience assessment, presenting some of the most relevant research currently underway, highlighting future challenges, and including several case studies.

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

Structural deformation monitoring and analysis by time-series InSAR;
Structural damage mapping by Lidar;
Structural reconstruction by remote sensing;
Remote sensing data processing;
Multi-source remote sensing fusion method and application;
Structural damage identification based on deep learning;
Structural resilience assessment based on damage mapping.

Dr. Xiaoqiong Qin
Dr. Ling Chang
Dr. Jie Dong
Dr. Xuguo Shi
Prof. Dr. Joan Ramon Casas Rius
Dr. Jónatas Valença
Guest Editors

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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

Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods in Remote Sensing (6 articles)

Published Papers (11 papers)

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Editorial

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2 pages, 164 KiB

Open AccessEditorial

Remote Sensing-Based Structural Health Monitoring and Damage Assessment

by Jónatas Valença, Xiaoqiong Qin, Ling Chang, Jie Dong, Xuguo Shi and Joan R. Casas

Remote Sens. 2024, 16(7), 1146; https://doi.org/10.3390/rs16071146 - 26 Mar 2024

Viewed by 1119

Abstract

With the rapid development of society and the economy, significant infrastructure, such as roads, buildings, high-speed railways, and bridges, have been built all over the world [...] Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

Research

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16 pages, 32785 KiB

Open AccessArticle

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

by Jin Deng, Keren Dai, Rubing Liang, Lichuan Chen, Ningling Wen, Guang Zheng and Hong Xu

Remote Sens. 2023, 15(18), 4538; https://doi.org/10.3390/rs15184538 - 15 Sep 2023

Cited by 7 | Viewed by 1279

Abstract

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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17 pages, 54932 KiB

Open AccessArticle

Revealing the Land Subsidence Deceleration in Beijing (China) by Gaofen-3 Time Series Interferometry

by Yakun Han, Tao Li, Keren Dai, Zhong Lu, Xinzhe Yuan, Xianlin Shi, Chen Liu, Ningling Wen and Xi Zhang

Remote Sens. 2023, 15(14), 3665; https://doi.org/10.3390/rs15143665 - 22 Jul 2023

Cited by 3 | Viewed by 1782

Abstract

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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29 pages, 15355 KiB

Open AccessArticle

Research on Dynamic Deformation Laws of Super High-Rise Buildings and Visualization Based on GB-RAR and LiDAR Technology

by Guojian Zhang, Zhiyang Wang, Wengang Sang, Baoxing Zhou, Zhiwei Wang, Guobiao Yao and Jingxue Bi

Remote Sens. 2023, 15(14), 3651; https://doi.org/10.3390/rs15143651 - 21 Jul 2023

Cited by 3 | Viewed by 1614

Abstract

It is well-known that structures composed of super high-rise buildings accumulate damages gradually due to ultra-long loads, material aging, and component defects. Thus, the bearing capacity of the structures can be significantly decreased. In addition, these effects may cause inestimable life and property losses upon strong winds, earthquakes, and other heavy loads. Hence, it is necessary to develop real-time health monitoring methods for super high-rise buildings to deeply understand the running state during operation, timely discover potential safety potentials, and to provide reference data for reinforcement design. Along these lines, in this work, the built super high-rise buildings (Yunding Building) and super high-rise buildings (the Main Tower of the Shandong International Financial Center), under construction, were selected as the research objects. The overall dynamic deformation laws of super high-rise buildings were monitored by using ground-based real aperture radar (GB-RAR) technology for its advantages in non-contact measurement, remote monitoring, and real-time display of observation results. Denoising of the observation data was also carried out based on wavelet analysis. The visualization of the space state of the Yunding Building was realized based on handheld LiDAR technology. From the acquired results, it was demonstrated that the measuring accuracy of GB-RAR could reach the submillimeter level, while the noises under a natural state of wavelet analysis were eliminated well. The maximum deformation values of the Yunding Building and the Main Tower of Shandong International Financial Center under their natural state were 9.63 mm and 16.46 mm, respectively. Under sudden wind loads, the maximum deformation of the Yunding Building could be as high as 895.79 mm. The overall motion state switched between an S-shaped pattern, hyperbolic-type, and oblique line, presented the characteristics of nonlinear elastic deformation. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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16 pages, 7121 KiB

Open AccessArticle

Surface Subsidence of Nanchang, China 2015–2021 Retrieved via Multi-Temporal InSAR Based on Long- and Short-Time Baseline Net

by Hua Gao, Luyun Xiong, Jiehong Chen, Hui Lin and Guangcai Feng

Remote Sens. 2023, 15(13), 3253; https://doi.org/10.3390/rs15133253 - 24 Jun 2023

Cited by 3 | Viewed by 3301

Abstract

Urban land subsidence threatens the safety of urban buildings and people’s lives. The time series interferometric synthetic aperture radar (InSAR) technology can provide us with large-area, high-resolution, and high-precision ground deformation monitoring. In this study, the time series InSAR technology and the strategy with long- and short-time baseline networking are used to obtain the surface deformation along the line of sight of Nanchang City based on the six-year (from December 2015 to December 2021) Sentinel-1 data. Longer datasets and better baseline strategies allow us to obtain more stable deformation results of Nanchang City than other researchers. The results of surface deformation show that the overall surface of Nanchang City is stable, but there are several obvious subsidence funnels. We carried out a field survey on four areas with significant surface subsidence. We considered that these subsidence areas may be related to soil compaction, building construction, and groundwater extraction. Based on the surface deformation results around the subway line, we analyzed the impact of subway construction on the surface along the line and identified the sections that need to be focused on by the managers to prevent the deformation area from affecting the surrounding buildings and subway line operation safety. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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16 pages, 28408 KiB

Open AccessArticle

StainView: A Fast and Reliable Method for Mapping Stains in Facades Using Image Classification in HSV and CIELab Colour Space

by Marta Torres-Gonzáles, Jónatas Valença, Bruno O. Santos, Ana Silva and Maria P. Mendes

Remote Sens. 2023, 15(11), 2895; https://doi.org/10.3390/rs15112895 - 1 Jun 2023

Cited by 5 | Viewed by 2366

Abstract

The new Construction 4.0 paradigm takes advantage of existing technologies. In this scope, the development and application of image-based methods for evaluating and monitoring the state of conservation of buildings has shown significant growth, including support for maintenance plans. Recently, powerful algorithms have been applied to automatically evaluate the state of conservation of buildings using deep learning frameworks, which are utilised as a black-box approach. The large amount of data required for training, the difficulty in generalising, and the lack of parameters to assess the quality of the results often make it difficult for non-experts to evaluate them. For several applications and scenarios, simple and more intuitive image-based approaches can be applied to support building inspections. This paper presents the StainView, which is a fast and reliable method. The method is based on the classification of the mosaic image, computed from a systematic acquisition, and allows one to (i) map stains in facades; (ii) locate critical areas; (iii) identify materials; (iv) characterise colours; and (v) produce detailed and comprehensive maps of results. The method was validated in three identical buildings in Bairro de Alvalade, in Lisbon, Portugal, that present different levels of degradation. The comparison with visual inspection demonstrates that StainView enables the automatic location and mapping of critical areas with high efficiency, proving to be a useful tool for building inspection: differences were of approximately 5% for the facade with the worst and average state of conservation, however, the values deteriorate for the facade under good conditions, reaching the double of percentage. In terms of processing speed, StainView allows a facade mapping that is 8–12 times faster, and this difference tends to grow with the number of evaluated façades. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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26 pages, 88791 KiB

Open AccessArticle

Surface Defect Detection of Nanjing City Wall Based on UAV Oblique Photogrammetry and TLS

by Jiayi Wu, Yufeng Shi, Helong Wang, Yajuan Wen and Yiwei Du

Remote Sens. 2023, 15(8), 2089; https://doi.org/10.3390/rs15082089 - 15 Apr 2023

Cited by 2 | Viewed by 2566

Abstract

Ancient architecture, with its long history, has a high cultural value, artistic achievement, and scientific value. The Nanjing City Wall was constructed in the mid-to-late 14th century, and it ranks first among the world’s city walls in terms of both length and size, whether historically or in the contemporary era. However, these sites are subject to long-term degradation and are sensitive to disturbances from the surrounding landscape, resulting in the potential deterioration of the architecture. Therefore, it is urgent to detect the defects and repair and protect Nanjing City Wall. In this paper, a novel method is proposed to detect the surface defects of the city walls by using the unmanned aerial vehicle (UAV) oblique photogrammetry and terrestrial laser scanning (TLS) data. On the one hand, the UAV oblique photogrammetry was used to collect the image data of the city wall, and a three-dimensional (3D) model of the wall was created using the oblique images. With this model, 43 cracks with lengths greater than 30 cm and 15 shedding surfaces with an area greater than 300 cm² on the wall can be effectively detected. On the other hand, the point cloud data obtained by TLS were firstly preprocessed, and then, the KNN algorithm was used to construct a local neighborhood for each sampling point, and the neighborhood was fitted using the least squares method. Next, five features of the point cloud were calculated, and the results were visualized. Based on the visualization results, surface defects of the wall were identified, and 18 cracks with lengths greater than 30 cm and 5 shedding surfaces with an area greater than 300 cm² on the wall were detected. To verify the accuracy of these two techniques in measuring cracks, the coordinates of some cracks were surveyed using a prism-free total station, and the lengths were calculated. The root mean square error (RMSE) of crack lengths based on the UAV oblique photogrammetry model and TLS point cloud model were calculated to be 0.73 cm and 0.34 cm, respectively. The results of the study showed that both techniques were able to detect the defects on the wall surface, and the measurement accuracy could meet the accuracy requirements of the surface defect detection of the city wall. Considering their low cost and high efficiency, these two techniques provide help for the mapping and conservation of historical buildings, which is of great significance for the conservation and repair of ancient buildings. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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18 pages, 87571 KiB

Open AccessArticle

Isostatic Anomaly and Isostatic Additional Force Analysis by Multiple Geodetic Observations in Qinling Area

by Huaqing Yuan, Yunlong Wu, Yi Zhang, Xuguo Shi and Shaofeng Bian

Remote Sens. 2023, 15(3), 740; https://doi.org/10.3390/rs15030740 - 27 Jan 2023

Cited by 2 | Viewed by 1644

Abstract

Determination of the isostatic anomaly and the isostatic additional force plays a key role in understanding the deep tectonic features and dynamics in the Qinling area. At present, high-accuracy observation gravity data are one of the important means to obtain the isostatic anomaly and the isostatic additional force. Firstly, we calculate the free-air gravity anomalies and the Bouguer gravity anomalies by using hybrid gravity and GPS observation data. Then, we invert the isostatic anomaly and the isostatic additional force. The results show that the isostatic depth calculated by Airy isostatic theory is 40–49 km, and the Moho depth is 39–48 km. The Weihe Basin is in a non-isostatic state with an upward isostatic additional force that reached about 20 MPa. The isostatic anomaly and the isostatic additional force are approximately zero in the northern Sichuan Basin, which indicates that the crust is in isostatic state. The negative isostatic anomaly and isostatic additional force in Liupanshan Mountains, the southwest margin of the Ordos Basin, and the local areas of the Qinling Orogen and Dabashan indicate the existence of crustal movement. By combining the measurement of InSAR, we obtain the surface deformation information of the Weihe Basin, as well as an upward trend, which proves that the result is highly consistent with the gravity observation. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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24 pages, 8515 KiB

Open AccessArticle

Sea-Crossing Bridge Detection in Polarimetric SAR Images Based on Windowed Level Set Segmentation and Polarization Parameter Discrimination

by Chun Liu, Chao Li, Jian Yang and Liping Hu

Remote Sens. 2022, 14(22), 5856; https://doi.org/10.3390/rs14225856 - 18 Nov 2022

Cited by 3 | Viewed by 1820

Abstract

As sea-crossing bridges are important hubs connecting separated land areas, their detection in SAR images is of great significance. However, under complex scenarios, the sea surface conditions, the distribution of coastal terrain morphologies, and the scattering components of different structures in the bridge area are very complex and diverse, which makes the accurate and robust detection of sea-crossing bridges difficult, including the sea–land segmentation and bridge feature extraction on which the detection depends. In this paper, we propose a polarimetric SAR image detection method for sea-crossing bridges based on windowed level set segmentation and polarization parameter discrimination. Firstly, the sea and land are segmented by a proposed windowed level set segmentation method, which replaces the construction of the level set segmentation energy function based on the isolated pixel distribution with a joint distribution of pixels in a certain window region. Secondly, water regions of interest are extracted by a proposed water region merging algorithm combining the distances of the water contour and polarization similarity parameter. Finally, the bridge regions of interest (ROIs) are extracted by merging close water contours, and the ROIs are discriminated by the polarimetric parameters of the polarization entropy and scattering angle. Experimental results using multiple AirSAR, RADARSAT-2, and TerraSAR-X quad-polarization SAR data from the coastal areas of San Francisco in the USA, Singapore, and Fuzhou, Fujian, and Zhanjiang, Guangdong, in China show that the proposed method can achieve 100% detection of sea-crossing bridges in different bands for different scenes, and the accuracy of the intersection of the ground-truth (IoG) index of bridge body recognition can reach more than 85%. The proposed method can improve the detection rate and reduce the false alarm rate compared with the traditional spatial-based method. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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16 pages, 5485 KiB

Open AccessArticle

A Novel Near-Real-Time GB-InSAR Slope Deformation Monitoring Method

by Yuhan Su, Honglei Yang, Junhuan Peng, Youfeng Liu, Binbin Zhao and Mengyao Shi

Remote Sens. 2022, 14(21), 5585; https://doi.org/10.3390/rs14215585 - 5 Nov 2022

Cited by 7 | Viewed by 2393

Abstract

In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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14 pages, 60779 KiB

Open AccessTechnical Note

Automatic Damage Detection and Diagnosis for Hydraulic Structures Using Drones and Artificial Intelligence Techniques

by Yantao Zhu and Hongwu Tang

Remote Sens. 2023, 15(3), 615; https://doi.org/10.3390/rs15030615 - 20 Jan 2023

Cited by 54 | Viewed by 3934

Abstract

Large-volume hydraulic concrete structures, such as concrete dams, often suffer from damage due to the influence of alternating loads and material aging during the service process. The occurrence and further expansion of cracks will affect the integrity, impermeability, and durability of the dam concrete. Therefore, monitoring the changing status of cracks in hydraulic concrete structures is very important for the health service of hydraulic engineering. This study combines computer vision and artificial intelligence methods to propose an automatic damage detection and diagnosis method for hydraulic structures. Specifically, to improve the crack feature extraction effect, the Xception backbone network, which has fewer parameters than the ResNet backbone network, is adopted. With the aim of addressing the problem of premature loss of image detail information and small target information of tiny cracks in hydraulic concrete structures, an adaptive attention mechanism image semantic segmentation algorithm based on Deeplab V3+ network architecture is proposed. Crack images collected from concrete structures of different types of hydraulic structures were used to develop crack datasets. The experimental results show that the proposed method can realize high-precision crack identification, and the identification results have been obtained in the test set, achieving 90.537% Intersection over Union (IOU), 91.227% Precision, 91.301% Recall, and 91.264% F1_score. In addition, the proposed method has been verified on different types of cracks in actual hydraulic concrete structures, further illustrating the effectiveness of the method. Full article

(This article belongs to the Special Issue Remote Sensing-Based Structural Health Monitoring and Damage Assessment)

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