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Remote Sensing in Structural Health Monitoring

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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 (31 January 2023) | Viewed by 33670

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Special Issue Editors

Prof. Dr. Yang Yang

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Guest Editor

School of Civil Engineering, Chongqing Univerity, Chongqing, China
Interests: bridge and structure inspection and reinforcement; structural health monitoring; structural vibration; seismic evaluation for structure
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Prof. Dr. Giuseppe Lacidogna

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Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24 -10129 Turin, Italy；European Academy of Sciences, Engineering Division, Brussels, Belgium
Interests: acoustic, electromagnetic, and particle emission energy; Acoustic emission methods for damage identification; concrete, masonry and rocks; cracking evolution in masonry arch bridges; creep behavior of concrete structures; critical phenomena from structural mechanics to geophysics; damage diagnosis in structures and construction materials; mechanics of proteins and macro-molecular structures; microcracking fracture propagation; static and dynamic analysis of high-rise buildings
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Dr. Mohamed Elchalakani

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Guest Editor

School of Engineering, Civil, Environmental and Mining Engineering, The University of Western Australia, Perth, Australia
Interests: dynamic and impact behaviour of steel and composite structures; sustainable construction materials; ECC, SCC, and 3D printing of composites; advanced reinforced geopolymer concrete structures; seismic design and performance of steel and concrete structures; advanced lightweight concrete structures; FRP concrete; steel–concrete CFT; composite structures; advanced rehabilitation and strengthening techniques using innovative polymers; nonlinear analysis of offshore pipelines and platform structures

Dr. Craig M. Hancock

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Guest Editor

School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: GNSS; ionospheric delay; digital construction; geospatial
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Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is the process of sensing, identification and evaluation of the damage and safety status of structures and their evolution laws by simulating the self-perception and self-diagnosis capabilities of the human body. It has been widely used in aerospace, civil engineering and mechanical engineering, among other fields. An SHM system generally includes sensors, data acquisition equipment, data transmission systems, databases for data management, data analysis and modeling modules, state evaluation and performance prediction modules, early warning equipment, visual user interfaces and software and operating systems. Often, SHM systems heavily rely on numerous remote sensing technologies, including photographs, RADAR, GNSS, lasers, acoustics, SONAR, etc.

This Special Issue focuses on applying remote sensing technology in various applications related to SHM. For example, SHM systems used for monitoring multiple types of civil infrastructure, mechanical equipment and other structural test data often rely on collecting remotely sensed data. Analyzing these data often requires real-time data transfer to a cloud platform for analysis. The data managed/processed in the cloud must be sourced directly from on-site sensors. Alternatively, if they are outsourced outside the structural domain (e.g., collection and cloud storage of environmental data, such as temperature, relative humidity etc.), these data must be compared with related structural measurements collected through on-site sensors (either mobile or installed).

Therefore, novel research is needed regarding the transmission and fusion mechanism of different data types; the reliability evaluation of online platform data for structural safety; novel sensors and data collection techniques; the accuracy, safety, and timeliness of transmission; damage identification and diagnosis methods; and sensor durability.

This Special Issue is expected to promote the exchange of views on the development opportunities of remote sensing technology for SHM. At the same time, creating innovative ideas in the field will facilitate a more streamlined connection between developing novel measuring technologies and bringing these technologies to the market. It is hoped that through this exchange of ideas, quality monitoring and testing within the engineering industry will be further improved by the use of novel approaches that utilize remote sensing technology.

We look forward to receiving your contributions.

Prof. Dr. Yang Yang
Prof. Dr. Giuseppe Lacidogna
Dr. Mohamed Elchalakani
Dr. Craig M. Hancock
Guest Editors

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Keywords

structural health monitoring
sensor
damage identification
cloud platform
data analysis
state evaluation
performance prediction
structural safety
measuring technologies

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Published Papers (12 papers)

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Research

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17 pages, 12467 KiB

Open AccessArticle

Remote 3D Displacement Sensing for Large Structures with Stereo Digital Image Correlation

by Weiwu Feng, Qiang Li, Wenxue Du and Dongsheng Zhang

Remote Sens. 2023, 15(6), 1591; https://doi.org/10.3390/rs15061591 - 15 Mar 2023

Cited by 2 | Viewed by 1914

Abstract

The work performance of stereo digital image correlation (stereo-DIC) technologies, especially the operating accuracy and reliability in field applications, is not fully understood. In this study, the key technologies of the field remote 3D displacement sensing of civil structures based on stereo-DIC have been proposed. An image correlation algorithm is incorporated in improving the matching accuracy of control points. An adaptive stereo-DIC extrinsic parameter calibration method is developed by fusing epipolar-geometry-based and homography-based methods. Furthermore, a reliable reference frame that does not require artificial markers is established based on Euclidean transformation, which facilitates in-plane and out-of-plane displacement monitoring for civil structures. Moreover, a camera motion correction is introduced by considering background points according to the camera motion model. With an experiment, the feasibility and accuracy of the proposed system are validated. Moreover, the system is applied to sense the dynamic operating displacement of a 2 MW wind turbine’s blades. The results show the potential capability of the proposed stereo-DIC system in remote capturing the full-field 3D dynamic responses and health status of large-scale structures. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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22 pages, 8007 KiB

Open AccessArticle

Structural Nonlinear Damage Identification Method Based on the Kullback–Leibler Distance of Time Domain Model Residuals

by Heng Zuo and Huiyong Guo

Remote Sens. 2023, 15(4), 1135; https://doi.org/10.3390/rs15041135 - 19 Feb 2023

Cited by 2 | Viewed by 1625

Abstract

Under external load excitation, damage such as breathing cracks and bolt loosening will cause structural time domain acceleration to have nonlinear features. To solve the problem of time domain nonlinear damage identification, a damage identification method based on the Kullback–Leibler (KL) distance of time domain model residuals is proposed in this paper. First, an autoregressive (AR) model order was selected using the autocorrelation function (ACF) and Akaike information criterion (AIC). Then, an AR model was obtained based on the structural acceleration response time series, and the AR model residual was extracted. Finally, the KL distance was used as a damage indicator to judge the structural damage source location. The effectiveness of the proposed method was verified by using a multi-story, multi-span stand model experiment and a simulated eight-story shear structure. The results show that the proposed structural nonlinear damage identification method can effectively distinguish the structural damage location of multi-degree-of-freedom shear structures and complex stand structures, and it is robust enough to detect environmental noise and small damage. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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13 pages, 10494 KiB

Open AccessCommunication

Vision-Based Dynamic Response Extraction and Modal Identification of Simple Structures Subject to Ambient Excitation

by Zhiwei Chen, Xuzhi Ruan and Yao Zhang

Remote Sens. 2023, 15(4), 962; https://doi.org/10.3390/rs15040962 - 9 Feb 2023

Cited by 3 | Viewed by 1831

Abstract

Vision-based modal analysis has gained popularity in the field of structural health monitoring due to significant advancements in optics and computer science. For long term monitoring, the structures are subjected to ambient excitation, so that their vibration amplitudes are quite small. Hence, although natural frequencies can be usually identified from the extracted displacements by vision-based techniques, it is still difficult to evaluate the corresponding mode shapes accurately due to limited resolution. In this study, a novel signal reconstruction algorithm is proposed to reconstruct the dynamic response extracted by the vision-based approach to identify the mode shapes of structures with low amplitude vibration due to environmental excitation. The experimental test of a cantilever beam shows that even if the vibration amplitude is as low as 0.01 mm, the first two mode shapes can be accurately identified if the proposed signal reconstruction algorithm is implemented, while without the proposed algorithm, they can only be identified when the vibration amplitude is at least 0.06 mm. The proposed algorithm can also perform well with various camera settings, indicating great potential to be used for vision-based structural health monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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20 pages, 20631 KiB

Open AccessArticle

A Hybrid Method for Vibration-Based Bridge Damage Detection

by Semih Gonen and Emrah Erduran

Remote Sens. 2022, 14(23), 6054; https://doi.org/10.3390/rs14236054 - 29 Nov 2022

Cited by 15 | Viewed by 3550

Abstract

Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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18 pages, 8445 KiB

Open AccessArticle

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

by Changqi Ji, Haili Sun, Ruofei Zhong, Jincheng Li and Yulong Han

Remote Sens. 2022, 14(19), 4791; https://doi.org/10.3390/rs14194791 - 25 Sep 2022

Cited by 7 | Viewed by 2301

Abstract

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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19 pages, 6443 KiB

Open AccessArticle

Separation of the Temperature Effect on Structure Responses via LSTM—Particle Filter Method Considering Outlier from Remote Cloud Platforms

by Yang Qin, Yingmin Li and Gang Liu

Remote Sens. 2022, 14(18), 4629; https://doi.org/10.3390/rs14184629 - 16 Sep 2022

Cited by 6 | Viewed by 1578

Abstract

Structural health monitoring (SHM) has been widely applied in the field of Mechanical and Civil Engineering in recent years. It is very hard to detect damage, however, using the measured data directly from the remote cloud platform of on-site structure, owing to changing environmental conditions. At the same time, outlier data from the remote cloud platform often occurs due to the harsh environmental conditions, interferences in the wireless medium, and the usage of low-quality sensors, which can greatly reduce the accuracy of structural health monitoring. In this paper, a novel temperature compensation method based on a long-short term memory (LSTM) network and the particle filter (PF) is proposed to separate the temperature effect from long-term structural health monitoring data. This method takes LSTMs as the state equation of PF, which solves the problem whereby PF cannot accurately derive the state equation for complex structures. A feedback model using the probability distribution generated by PF is developed to filter the observed value, thus measurement outliers can be successfully reduced. A numerical simulation and the measured deflection data from an SHM system are utilized to verify the proposed method. Results from the numerical simulation show that the LSTM-PF method can satisfactorily compensate for the temperature effect even when the nonlinear temperature effect is considered. Moreover, outputs from the SHM system of a large-scale suspension bridge indicate the temperature effect can be compensated and outliers can be appropriately reduced at the same time using the measured deflection data. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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25 pages, 12916 KiB

Open AccessArticle

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

by Wanrun Li, Wenhai Zhao, Jiaze Gu, Boyuan Fan and Yongfeng Du

Remote Sens. 2022, 14(13), 3113; https://doi.org/10.3390/rs14133113 - 28 Jun 2022

Cited by 10 | Viewed by 2544

Abstract

The structural condition of blades is mainly evaluated using manual inspection methods. However, these methods are time-consuming, labor-intensive, and costly, and the detection results significantly depend on the experience of inspectors, often resulting in lower precision. Focusing on the dynamic characteristics (i.e., natural frequencies) of large wind turbine blades, this study proposes a monitoring method based on the target-free DSST (Discriminative Scale Space Tracker) vision algorithm and UAV. First, the displacement drift of UAV during hovering is studied. Accordingly, a displacement compensation method based on high-pass filtering is proposed herein, and the scale factor is adaptive. Then, the machine learning is employed to map the position and scale filters of the DSST algorithm to highlight the features of the target image. Subsequently, a target-free DSST vision algorithm is proposed, in which illumination changes and complex backgrounds are considered. Additionally, the algorithm is verified using traditional computer vision algorithms. Finally, the UAV and the target-free DSST vision algorithm are used to extract the dynamic characteristic of the wind turbine blades under shutdown. Results show that the proposed method can accurately identify the dynamic characteristics of the wind turbine blade. This study can serve as a reference for assessment of the condition of wind turbine blades. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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23 pages, 1771 KiB

Open AccessArticle

Full-Scale Highway Bridge Deformation Tracking via Photogrammetry and Remote Sensing

by William Graves, Kiyarash Aminfar and David Lattanzi

Remote Sens. 2022, 14(12), 2767; https://doi.org/10.3390/rs14122767 - 9 Jun 2022

Cited by 13 | Viewed by 3754

Abstract

Recent improvements in remote sensing technologies have shown that techniques such as photogrammetry and laser scanning can resolve geometric details at the millimeter scale. This is significant because it has expanded the range of structural health monitoring scenarios where these techniques can be used. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be used to evaluate the performance of a highway bridge during a static load test. Various point cloud registration and deformation tracking algorithms are explored. Included is an introduction to a novel deformation tracking algorithm that uses the interpolation technique of kriging as the basis for measuring the geometric changes. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. The results demonstrate a pathway from the collection of digital photographs to a mechanical analysis with results that capture the bridge deformation within one standard deviation of the mean reported value. These results are promising given that the midspan bridge deformation for the load test is only a few millimeters. Ultimately, the approaches evaluated in this work yielded errors on the order of 1 mm or less for ground truth deflections as small as 3.5 mm. Future work for this method will investigate using these results for updating finite element models. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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17 pages, 8443 KiB

Open AccessArticle

Research on Lightweight Disaster Classification Based on High-Resolution Remote Sensing Images

by Jianye Yuan, Xin Ma, Ge Han, Song Li and Wei Gong

Remote Sens. 2022, 14(11), 2577; https://doi.org/10.3390/rs14112577 - 27 May 2022

Cited by 12 | Viewed by 2339

Abstract

With the increasing frequency of natural disasters becoming, it is very important to classify and identify disasters. We propose a lightweight disaster classification model, which has lower computation and parameter quantities and a higher accuracy than other classification models. For this purpose, this paper specially proposes the SDS-Network algorithm, which is optimized on ResNet, to deal with the above problems of remote sensing images. First, it implements the spatial attention mechanism to improve the accuracy of the algorithm; then, the depth separable convolution is introduced to reduce the number of model calculations and parameters while ensuring the accuracy of the algorithm; finally, the effect of the model is increased by adjusting some hyperparameters. The experimental results show that, compared with the classic AlexNet, ResNet18, VGG16, VGG19, and Densenet121 classification models, the SDS-Network algorithm in this paper has a higher accuracy, and when compared with the lightweight models mobilenet series, shufflenet series, squeezenet series, and mnasnet series, it has lower model complexity and a higher accuracy rate. According to a comprehensive performance comparison of the charts made in this article, it is found that the SDS-Network algorithm is still better than the regnet series algorithm. Furthermore, after verification with a public data set, the SDS-Network algorithm in this paper is found to have a good generalization ability. Thus, we can conclude that the SDS-Network classification model of the algorithm in this paper has a good classification effect, and it is suitable for disaster classification tasks. Finally, it is verified on public data sets that the proposed SDS-Network has good generalization ability and portability. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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Review

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25 pages, 3607 KiB

Open AccessReview

Research Progress of SHM System for Super High-Rise Buildings Based on Wireless Sensor Network and Cloud Platform

by Yang Yang, Wenming Xu, Zhihao Gao, Zhou Yu and Yao Zhang

Remote Sens. 2023, 15(6), 1473; https://doi.org/10.3390/rs15061473 - 7 Mar 2023

Cited by 12 | Viewed by 3308

Abstract

In recent years, the number of super high-rise buildings is increasing due to the rapid development of economy and construction technology. It is important to evaluate the health condition of super high-rise buildings to make them operate safely. However, conventional structural health monitoring (SHM) system requires a great number of wires to connect the sensors, power sources, and the data acquisition equipment, which is an extremely difficult process to plan the layout of all wires. Hence, one of the usually used compromising approaches is to limit the number of sensors to reduce the usage of wires. Recently, wireless sensor networks and cloud platform have been widely used in SHM system for super high-rise buildings because of their convenient installation, low maintenance cost, and flexible deployment. This paper presents a comprehensive review of the existing SHM system for super high-rise buildings based on wireless sensor network and cloud platform, which usually consists of sensing network subsystem, data acquisition subsystem, data transmission subsystem, and condition evaluation subsystem. This paper also reviews the crucial techniques and typical examples of SHM system used for famous super high-rise buildings. In addition, the existing difficulties in wireless sensor network and cloud platform based SHM system for super high-rise buildings and the future research directions are discussed and summarized. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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Other

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14 pages, 23724 KiB

Open AccessTechnical Note

3D Reconstruction from 2D Plans Exemplified by Bridge Structures

by Kwasi Nyarko Poku-Agyemang and Alexander Reiterer

Remote Sens. 2023, 15(3), 677; https://doi.org/10.3390/rs15030677 - 23 Jan 2023

Cited by 12 | Viewed by 3176

Abstract

Due to increasing traffic on roads and railways, the maintenance of bridges is becoming more and more important. Building Information Modelling (BIM) provides the perfect basis to efficiently plan these maintenance activities. However, for historic bridges, which moreover require intensive maintenance, there is no BIM available. The demand to digitize these bridges is correspondingly high. Further, to the already existing measurement methods (laser scanning, photogrammetry, etc.), a novel workflow for the digitalization of bridges from 2D plans is presented. Based on image processing for corner detection, 3D point cloud reconstruction of parts and fusion of reconstructed parts can be used create a 3D object to scale. The point cloud can serve as a supplement to as-built laser scanning or camera data. The presented method was evaluated based on three bridges over the Dreisam river in Freiburg, Germany. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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21 pages, 6486 KiB

Open AccessTechnical Note

Auto-Diagnosis of Time-of-Flight for Ultrasonic Signal Based on Defect Peaks Tracking Model

by Fan Yang, Dongliang Shi, Long-Yin Lo, Qian Mao, Jiaming Zhang and Kwok-Ho Lam

Remote Sens. 2023, 15(3), 599; https://doi.org/10.3390/rs15030599 - 19 Jan 2023

Cited by 9 | Viewed by 2553

Abstract

With the popularization of humans working in tandem with robots and artificial intelligence (AI) by Industry 5.0, ultrasonic non-destructive testing (NDT)) technology has been increasingly used in quality inspections in the industry. As a crucial part of handling ultrasonic testing results–signal processing, the current approach focuses on professional training to perform signal discrimination but automatic and intelligent signal optimization and estimation lack systematic research. Though the automated and intelligent framework for ultrasonic echo signal processing has already exhibited essential research significance for diagnosing defect locations, the real-time applicability of the algorithm for the time-of-flight (ToF) estimation is rarely considered, which is a very important indicator for intelligent detection. This paper conducts a systematic comparison among different ToF algorithms for the first time and presents the auto-diagnosis of the ToF approach based on the Defect Peaks Tracking Model (DPTM). The proposed DPTM is used for ultrasonic echo signal processing and recognition for the first time. The DPTM using the Hilbert transform was verified to locate the defect with the size of 2–10 mm, in which the wavelet denoising method was adopted. With the designed mechanical fixture through 3D printing technology on the pipeline to inspect defects, the difficulty of collecting sufficient data could be conquered. The maximum auto-diagnosis error could be reduced to 0.25% and 1.25% for steel plate and pipeline under constant pressure, respectively, which were much smaller than those with the DPTM adopting the cross-correlation. The real-time auto-diagnosis identification feature of DPTM has the potential to be combined with AI in future work, such as machine learning and deep learning, to achieve more intelligent approaches for industrial health inspection. Full article

(This article belongs to the Special Issue Remote Sensing in Structural Health Monitoring)

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