Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Vashpanov, Yuriy; Son, Jung‐Young; Heo, Gwanghee; Podousova, T.; Kim, Yong Suk

doi:10.1155/2019/2398124

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Due to the diverse bridge surfaces, classical image processing and classical machine learning are not able to deal with diverse bridge scenarios. Therefore, recent works use image processing based on convolutional neural networks (Vignesh et al, 2021), (Xu et al, 2019), (Vashpanov et al, 2019), (Li et al, 2020), (Li et al, 2021). (An et al, 2021) use a combination of a convolutional neural network and cluster segmentation.…”

Section: The Current Situation Regarding Crack Detection and Localiza...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

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Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.

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Section: The Current Situation Regarding Crack Detection and Localiza...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

View full text Add to dashboard Cite

show abstract

“…Several works have also been identified to utilize RGB color information for crack detection of the building surface. For example, Kim et al [41] investigated cracks on concrete structures using an RGB-D camera based on the angle of view, Sanchez and Bairan [42] performed crack pattern analysis on concrete elements based on RGB images and orientation kernels, and Vashpanov [43] and Barazeetti and Scaioni [44] conducted crack measurements using a high-resolution digital camera with photogrammetry techniques. These studies, however, only employ image-based methods, which depend on the camera lens, focal length, and the quality of the pixel size taken during the dataset collection [44].…”

Section: Related Workmentioning

confidence: 99%

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

et al. 2022

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To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In the existing literature, the geometric properties of point clouds are still the main focus for 3D reconstruction, while the surface performance of the dataset is of less interest due to the partial and limited analysis performed by certain disciplines. As a consequence, geometric defects on surface datasets are often identified when visible through physical inspection. In response to that, this study presents an integrated approach for investigating the materials behavior of heritage building surfaces by making use of attribute point cloud information (i.e., XYZ, RGB, reflection intensity). To do so, fracture surface analysis and material properties are computed to identify vulnerable structures on the existing dataset. This is essential for architects or conservators so that they can assess and prepare preventive measures to minimize microclimatic impacts on the buildings.

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“…The crack's width, depth, and morphology were constrained by the image's pixel intensity distribution. To perform the estimation, the image was converted to 16-bit gray scale and then a mathematical relationship between the intensity distribution and the depth and width of the enhanced image was derived [35].…”

Section: Literature Surveymentioning

confidence: 99%

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

Jayaraju¹,

Somasundaram²,

Suprakash³

et al. 2022

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Buildings expand and contract in response to their environment, which results in cracks in the structure. This can pose a serious threat to the people who use it, and these movements are frequently too small to be observed, and thus go unnoticed. Cracks can be caused by a variety of factors, including defects in the construction process, ground movement, foundation failure, and decay of the building fabric. If a structure is unable to accommodate this movement, cracking is likely to occur, posing a serious risk to the building's structural integrity. Only after cracks are identified can they be treated, and existing manual methods of sketching the crack patterns are highly subjective to the person performing the analysis, are frequently constrained by high costs, equipment and tool availability, and are extremely time consuming. In this paper, 40,000 images divided into two and categorized into positive and negative cracks are used as input and the presence of cracks is detected using a deep learning technique. The following crack types are included in the experimentation: hairline, stepped, vertical, and horizontal. In comparison to conventional image processing and other deep learning-based techniques, the proposed Convolutional Neural Network (CNN) achieves significantly higher accuracy than the Recurrent Neural Network (RNN). This paper’s objective is to create a model which can detect the cracks through deep learning methodology, and this will be the innovative region in crack detection using neural net framework.

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Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Cited by 10 publications

References 27 publications

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

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