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

Bazrafshan, Pedram; On, Thinh; Basereh, Sina; Okumus, Pinar; Ebrahimkhanlou, Arvin

doi:10.1111/mice.13009

Cited by 14 publications

(6 citation statements)

References 99 publications

(154 reference statements)

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“…This paper follows the image-to-graph conversion approach outlined in an earlier work by the authors [23]. A summary of the approach is presented here.…”

Section: Crack-to-graph Conversion Processmentioning

confidence: 99%

“…Hamidia et al [22] developed a machine-learning procedure for automated damage state identification of non-ductile reinforced concrete moment frames using visual indices from concrete surface crack patterns. Bazrafshan et al [23] introduced a novel approach for evaluating damage in reinforced concrete shear walls by transforming crack patterns into graph representations and applying graph theory for damage quantification.…”

Section: Introductionmentioning

confidence: 99%

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Detection of cracking mechanism transition on reinforced concrete shear walls using graph theory

Bazrafshan,

Ebrahimkhanlou

2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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Concrete crack quantification is a crucial step toward the assessment of concrete structures. Although many computer vision algorithms have been developed to detect cracks and measure their properties, such as width and length, interpretation of the detected cracks in terms of structural behavior remains a challenge. Specifically, identifying the onset of changes in the behavior mechanism (e.g., shear or flexural) of the structure is of great interest. This is particularly important in concrete shear walls subject to cyclic loading, in which cracks may close and thus cause crack width measurements to be unreliable. In such structures, individual and disjointed cracks gradually form mosaic patterns. The transition from one state to the other results in a sudden change in the cracking patterns. This study builds upon the previous work of the authors and uses graph theory to represent concrete crack patterns. The main idea is to utilize graph features and their changes to track changes in the crack patterns. To validate the proposed method, surface crack images of 15 large-scale reinforced concrete shear walls under cyclic loads are used. For each wall, the images include crack patterns at different load levels. Using the proposed methodology, the images of the crack images are converted to their representative graph. Afterward, two specific graph features are extracted: 1) the average degree of network (k_avg) and 2) the weighted average degree of network (kw_avg). The ratio of k_avg/kw_avg versus the drift of the walls at each load cycle is used to detect the change in the cracking mechanism. Results show that the minimum value of the ratio corresponds to the change in the cracking mechanism. The robustness of the proposed metric indicates that it can be used for training machine learning models to develop systems that can automatically signal the onset of a change in the cracking mechanism.

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“…This paper follows the image-to-graph conversion approach outlined in an earlier work by the authors [23]. A summary of the approach is presented here.…”

Section: Crack-to-graph Conversion Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of cracking mechanism transition on reinforced concrete shear walls using graph theory

Bazrafshan,

Ebrahimkhanlou

2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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“…Capturing the initiation and evolution of cracks in the structures is of utmost importance to safeguard the structures. Among the nondestructive evaluation (NDE) methods (e.g., vision-based [1][2][3], ultrasound [4,5]), acoustic emission (AE) has shown great success [6][7][8][9][10][11][12]. In using AE data, researchers have used RA values versus AF to distinguish the shift in the cracking mechanisms within a structure [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An entropy-based probabilistic model for acoustic emission RA-value-average ‎frequency data

Bazrafshan,

Ebrahimkhanlou

2024

Health Monitoring of Structural and Biological Systems XVIII

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This study presents an innovative method for probabilistic modeling of acoustic emission by applying the principles of maximum entropy and utilizing a non-Gaussian probability distribution. The focus is on mathematical modeling of the RA (rise time to amplitude ratio) values versus the average frequency (AF) using a fourth-order non-Gaussian probability distribution. The main goal is to introduce this probabilistic model for AE data and showcase some of its benefits. Specifically, the model can 1) identify cracks at early stages and 2) distinguish between shear cracks and tensile (flexural) cracks within concrete and wood. To confirm the effectiveness of the proposed model, experimental data from acoustic emission tests on a concrete slab and a wood specimen are used. The parameters of the proposed probabilistic model capture the transition from one cracking mode to another. The results demonstrate the model's success in detecting the transition of cracks from tensile to shear at different stages. Capturing the evolution of the cracks is made possible by incorporating the parameter of time alongside the proposed fourth-order probabilistic model. These results indicate that the suggested method is a powerful mathematical approach for probabilistic modeling of RA-AF plots of AE data.

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“…Further, the literature reveals that there is a noticeable gap in terms of automating quality checks, with current procedures largely dependent on human evaluation. Existing studies on automation in construction mainly focus on robotics and machine learning applications, [12][13][14][15][16][17][18] with limited application to connections in mass timber construction.…”

Section: Introductionmentioning

confidence: 99%

A laser-based quality control of screw penetration in mass timber connections

Ghadimzadeh Alamdari,

Aghayere,

Ebrahimkhanlou

2024

Health Monitoring of Structural and Biological Systems XVIII

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Mass timber is a relatively new and sustainable construction material that can carry large loads. Such loads require new connections that often use very long screws. However, ensuring their mechanical integrity against overtightening is paramount for structural safety. Traditional manual evaluations present limitations. This research employs laser scanning technology for automated evaluation of screw penetration in wood-aluminum interfaces using VGS9180 screws. By applying torque ranging from 10 to 50 Nm in 5 Nm intervals, a critical torque-penetration correlation emerged, highlighting a sensitive range between 35-40 Nm. Within this range, small overtightening can jeopardize the integrity of the connection. The study underscores laser scanning's efficacy in pinpointing improperly torqued screws, offering a potential leap in quality assurance for timber construction. The results of this study indicate a threshold-based method can detect overtightened screws on the experimental setup when the distance between two surfaces falls beyond 3.2 mm.

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

Cited by 14 publications

References 99 publications

Detection of cracking mechanism transition on reinforced concrete shear walls using graph theory

Detection of cracking mechanism transition on reinforced concrete shear walls using graph theory

An entropy-based probabilistic model for acoustic emission RA-value-average ‎frequency data

A laser-based quality control of screw penetration in mass timber connections

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