Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images

Entezami, Alireza; Michele, Carlo De; Arslan, Ali; Behkamal, Bahareh

doi:10.3390/s22134964

Cited by 10 publications

(4 citation statements)

References 41 publications

(66 reference statements)

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“…In particular, the satellite-borne Aperture forming radar (SAR) can be used to extract the modal features of bridges such as natural frequencies and mode shapes even in very harsh weather conditions. 150 Entezami et al 151 proposed a hybrid unsupervised learning and satellite image-based method for early damage detection of bridges. Extracted displacement samples from satellite images were enhanced and fed into the autoencoder for the reconstruction of the input.…”

Section: Other Types Of Data-oriented Methodsmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Other Types Of Data-oriented Methodsmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…SAR utilizes microwave signals to penetrate cloud cover, detecting subtle deformations in structures and monitoring ground displacement. SAR is particularly valuable for monitoring large areas and regions with frequent cloud cover, making it applicable to diverse SHM applications [94][95][96][97]. In addition, Interferometric Synthetic Aperture Radar (InSAR) is an advanced technique analyzing interference patterns from multiple SAR images.…”

Section: Satellite Technologymentioning

confidence: 99%

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Kim,

Choi,

Kim

et al. 2024

J. Imaging

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The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

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“…The significant variability in conditions in SHM are mainly caused by environmental and operational changes. In most cases, variations in temperature, humidity, wind characteristics, and live loads (e.g., traffic) are the major reasons for changes in the measured data or features extracted from such data (e.g., modal parameters [17], displacements extracted from SAR images [18,19]).…”

Section: Introductionmentioning

confidence: 99%

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

et al. 2023

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Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.

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Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images

Cited by 10 publications

References 41 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

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