Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

Mousa, Mohammed A.; Yussof, Mustafasanie M.; Udi, Ufuoma Joseph; Nazri, Fadzli Mohamed; Kamarudin, Mohd Khairul Amri; Parke, G A; Assi, Lateef; Ghahari, SeyedAli

doi:10.3390/infrastructures6120176

Cited by 38 publications

(16 citation statements)

References 107 publications

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“…This study proposed an accurate method for full-field vibration measurement. The method is based on projected 3D-DIC and is capable of non-invasively identifying the full-field mode shapes and natural frequencies of a UAV wing made of composite material, which could potentially benefit structural dynamics and health monitoring applications [54]. Previous studies described fringe patterns [55,56], laser speckle [41,57] or a combination of both [40] to create a non-invasive pattern for full-field vibration measurement in DIC applications.…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

Janeiro

Fernández-López

Manguán

et al. 2022

Sensors

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Non-contact vibration measurements are relevant for non-invasively characterizing the mechanical behavior of structures. This paper presents a novel methodology for full-field vibrational analysis at high frequencies using the three-dimensional digital image correlation technique combined with the projection of a speckle pattern. The method includes stereo calibration and image processing routines for accurate three-dimensional data acquisition. Quantitative analysis allows the extraction of several deformation parameters, such as the cross-correlation coefficients, shape and intensity, as well as the out-of-plane displacement fields and mode shapes. The potential of the methodology is demonstrated on an Unmanned Aerial Vehicle wing made of composite material, followed by experimental validation with reference accelerometers. The results obtained with the projected three-dimensional digital image correlation show a percentage of error below 5% compared with the measures of accelerometers, achieving, therefore, high sensitivity to detect the dynamic modes in structures made of composite material.

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Section: Discussionmentioning

confidence: 99%

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

Janeiro

Fernández-López

Manguán

et al. 2022

Sensors

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“…Recently, SHM has been accomplished by the analysis of images and videos taken by modern sensors such as cameras, robotic sensors, telephones, and drones [20]. Regardless of the type of sensor, the accuracy of the existing damage diagnosis and localization algorithms heavily depends on the accessibility of several high-quality sensors and datasets.…”

Section: Related Workmentioning

confidence: 99%

Bridge Damage Identification Using Deep Neural Networks on Time–Frequency Signals Representation

Santaniello,

Russo

2023

Sensors

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For the purpose of maintaining and prolonging the service life of civil constructions, structural damage must be closely monitored. Monitoring the incidence, formation, and spread of damage is crucial to ensure a structure’s ongoing performance. This research proposes a unique approach for multiclass damage detection using acceleration responses based on synchrosqueezing transform (SST) together with deep learning algorithms. In particular, our pipeline is able to classify correctly the time series representing the responses of accelerometers placed on a bridge, which are classified with respect to different types of damage scenarios applied to the bridge. Using benchmark data from the Z24 bridge for multiclass classification for different damage situations, the suggested method is validated. This dataset includes labeled accelerometer measurements from a real-world bridge that has been gradually damaged by various conditions. The findings demonstrate that the suggested approach is successful in exploiting pre-trained 2D convolutional neural networks, obtaining a high classification accuracy that can be further boosted by the application of simple voting methods.

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“…They used the El Carrizo Bridge as a case study, and their results showed the precision of PPP-GNSS measurement technology as well as the potential advantages of the alternative probabilistic SHM technique. Mousa et al [36] provided an overview of the vision and digital image correlation (DIC)-based SHM techniques for bridges. In their study, they considered different kinds of bridges, such as concrete, suspension, masonry, and steel bridges.…”

Section: The Importance Of Using the Shm Systemmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

Cited by 38 publications

References 107 publications

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

Bridge Damage Identification Using Deep Neural Networks on Time–Frequency Signals Representation

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

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