Surface crack detection in concrete structures using video processing techniques

Momeni, Hamed; Basereh, Sina; Okumus, Pinar; Ebrahimkhanlou, Arvin

doi:10.1117/12.2583323

Cited by 7 publications

(3 citation statements)

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“…Liu & Xu, 2022; Y. Liu & Gao, 2022;Momeni et al, 2021;Y. Zhang & Yuen, 2021) methods to develop objective methods and less biased and prone to error.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the advancements in technology with the advent of fast processors and more in‐hand memory have paved the way for artificial intelligence (AI) to be implemented in structural health monitoring and nondestructive evaluation. Hence, researchers implemented machine learning (ML—Adeli & Kim, 2001; Bang et al., 2019; Hu et al., 2021; Lim et al., 2018; C. Luo et al., 2021; Rafiei et al., 2016; Y. Xie et al., 2020), deep learning (Chu et al., 2022; Kang et al., 2020; Li et al., 2018; Ni et al., 2019; Rafiei & Adeli, 2018; Rafiei et al., 2017a, 2017b; Żarski et al., 2022; C. Zhang et al., 2020; D. Zou et al., 2022), and computer vision (Das et al., 2021; Ebrahimkhanlou et al., 2016b; Jahanshahi & Masri, 2012; X. Kong & Li, 2018; Lattanzi et al., 2012; C. Liu & Xu, 2022; Y. Liu & Gao, 2022; Momeni et al., 2021; Y. Zhang & Yuen, 2021) methods to develop objective methods and less biased and prone to error. Among these methods for quantification of concrete cracks, crack length (Adhikari et al., 2014; Flah et al., 2020; Yang et al., 2018), crack width and orientation (Jahanshahi & Masri, 2013; S. Y. Kong et al., 2021; A. Zhang et al., 2017; Zhu et al., 2011), crack density (Asjodi et al., 2022; Pan et al., 2022; Patzelt et al., 2022), and fractal and multi‐fractal (Athanasiou et al., 2020; Ebrahimkhanlou et al., 2019; Rezaie, Mauron, & Beyer, 2020; Wang et al., 2017) are investigated.…”

Section: Introductionmentioning

confidence: 99%

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A graph‐based method for quantifying crack patterns on reinforced concrete shear walls

Bazrafshan

Basereh

et al. 2023

Computer aided Civil Eng

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This paper presents an innovative method to quantify damage based on surface cracks of reinforced concrete shear walls (RCSWs). The key idea is to use artificial intelligence and convert crack patterns to graphs. In this method, the mathematics of graph theory is used to extract information (graph‐based features) from crack patterns and use them for crack quantification. The proposed graph features are used in linear regression and leave‐one‐out cross‐validation to predict the mechanical features calculated for each RCSW: Park and Ang damage index and the dissipated energy. Among the three general stages of damage, which are safe, questionable, and not safe, this paper focuses on quantifying the second stage. To validate the approach, crack images of three RCSWs are used. The walls had different aspect ratios (0.54, 0.94, and 2.00) and were subject to quasi‐static cyclic loading. Regression results demonstrate low root mean squared errors and high coefficients of determination (R2 scores above 0.845). This proves the ability of the proposed graph‐based method in quantifying damage based on surface crack patterns.

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“…Liu & Xu, 2022; Y. Liu & Gao, 2022;Momeni et al, 2021;Y. Zhang & Yuen, 2021) methods to develop objective methods and less biased and prone to error.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A graph‐based method for quantifying crack patterns on reinforced concrete shear walls

Bazrafshan

Basereh

et al. 2023

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

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“…In this way, the present study is a precursor step before crack quantification algorithms. [5][6][7][8][9] The main idea in the current paper is the integration of data collected from depth cameras and LiDARs. Such sensors are becoming ubiquitously available on robotic platforms and VR devices.…”

Section: Introductionmentioning

confidence: 99%

A robotic approach for crack detection through the integration of cameras and LiDARs

Alamdari

Ebrahimkhanlou

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, image processing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.

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A Video-Based Crack Detection in Concrete Surfaces

Momeni

Basereh

Okumus

et al. 2021

Data Science in Engineering, Volume 9

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Surface crack detection in concrete structures using video processing techniques

Cited by 7 publications

References 0 publications

A graph‐based method for quantifying crack patterns on reinforced concrete shear walls

A graph‐based method for quantifying crack patterns on reinforced concrete shear walls

A robotic approach for crack detection through the integration of cameras and LiDARs

A Video-Based Crack Detection in Concrete Surfaces

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