A Brief Review and a New Graph-Based Image Analysis for Concrete Crack Quantification

Payab, Mahsa; Abbasina, Reza; Khanzadi, Mostafa

doi:10.1007/s11831-018-9263-6

Cited by 36 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zou et al, 2012), rock surfaces (Ai et al, 2020;Z. Luo et al, 2015), and concrete (Payab et al, 2019). In particular, Fernandes and Ciobanu (2014) presented a graph-based method to classify pavement cracks and use them for learning predictive models.…”

Section: Introductionmentioning

confidence: 99%

“…Graph theory has a rich history of being used in diverse fields of expertise (Ahmadlou & Adeli, 2017; delEtoile & Adeli, 2017; Mozaffarilegha & Adeli, 2019). Specifically, in engineering, graphs have previously been used for crack detection purposes in pavements (Amhaz et al., 2016; Q. Zou et al., 2012), rock surfaces (Ai et al., 2020; Z. Luo et al., 2015), and concrete (Payab et al., 2019). In particular, Fernandes and Ciobanu (2014) presented a graph‐based method to classify pavement cracks and use them for learning predictive models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bazrafshan

Basereh

et al. 2023

Computer aided Civil Eng

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The first group adopts pixel count as a quantitative metric for representing cracks. Payab et al [ 33 ] expressed the crack area and length values in pixel numbers of crack region and skeleton, respectively, and took the ratio of the two as the average crack width. The second type entails a scale factor to convert the output of the first group into actual physical dimensions.…”

Section: Introductionmentioning

confidence: 99%

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Zhang,

Shen,

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder–decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from −3.9% to 36.0%, indicating the practical feasibility of our proposed method.

show abstract

“…Image processing technique can provide objective results quickly (Abouhamad et al, 2017;Mohan and Poobal, 2018;Payab et al, 2019;Chen et al, 2019;Tang et al, 2019). The identification and quantification of FFC by multi-intelligent body fuzzy systems (Payab and Khanzadi, 2021), the defects identification in the FFC appearance quality by laser point thermography (Scalbi et al, 2019), and the recording of the cracks beginning and development in combination with timelines (Prasanna et al, 2016) have been implemented.…”

Section: Introductionmentioning

confidence: 99%

Chromatic Aberration Identification of Fair-Faced Concrete Research Based on Multi-Scale Lightweight Structured Data Algorithm

et al. 2022

View full text Add to dashboard Cite

Chromatic aberration is one of the quality defects in the appearance of fair-faced concrete (FFC). The mainly surface chromatic aberration identification (CAI) method being applied is manual observation, which is subjective and time-consuming. A multi-scale lightweight structured data algorithm (MSLSDA) for CAI in FFC is proposed in this manuscript. An unmanned aerial vehicle (UAV) is used for image acquisition. 2368 FFC sample images are collected to build the datasets. The FFC chromatic aberration features are identified by the improved Residual Network Convolutional Neural Network (CNN) framework to achieve chromatic aberration samples quantitative analysis. The method proposed in this manuscript can verify the generalization prediction ability of the MSLSDA for different building samples by generalization prediction set. The results show that the accuracy in CAI samples and chromatic aberration generalization prediction samples can achieve 92.1 and 99.6%, respectively. The FFC chromatic aberration detection platform (FFC-CADP) built by color space conversion, histogram equalization, image color recognition, image noise reduction and image mask algorithm is able to calculate boundary features, geometric parameter features (length and width), chromatic aberration ratio features, total chromatic aberration ratio and number of chromatic aberration.

show abstract

A Brief Review and a New Graph-Based Image Analysis for Concrete Crack Quantification

Cited by 36 publications

References 30 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 Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Chromatic Aberration Identification of Fair-Faced Concrete Research Based on Multi-Scale Lightweight Structured Data Algorithm

Contact Info

Product

Resources

About