Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

Hamidia, Mohammadjavad; Afzali, Mobinasadat; Jamshidian, Sara; Safi, Mohammad

doi:10.1002/suco.202200351

Cited by 20 publications

(4 citation statements)

References 78 publications

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“…The values in the fourth and sixth columns of Table 3 are calculated by multiplying the sensitivity value with the positive or negative percentage. Noteworthy to mention that previous studies have widely incorporated symbolic regression method 24,28,34,[39][40][41][42][43][44]86,96 or machine learningbased models 45,46,92,94,95 for correlating the image-derived parameters to the level of damage in the structural components. The machine learning-based models are conventionally used when very complex relationship exists between the predictors.…”

Section: Plan Iii: Modeling By Two Gfds and Aspect Ratiomentioning

confidence: 99%

“…In a recent study, Hamidia et al 40 utilized GFDs derived from crack maps of damaged joints in RCMFs to assess and evaluate the extent of damage. Hamidia and Ganjizadeh utilized fractal-based procedures to quantify the peak drift ratio 41 and post-earthquake residual stiffness 42 in nonductile RCMF joints. GFDs have been also incorporated for the estimation of residual stiffness, 39,43 and strength 44 in RC columns.…”

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confidence: 99%

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Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns

Hamidia,

Jamshidian,

Afzali

et al. 2023

Structural Design Tall Build

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SummaryThis paper presents a novel method for estimating the seismic peak interstory drift ratio (IDR) in reinforced concrete (RC) columns after an earthquake using surface crack image analysis. The quantitative representation of the complexity and irregularity of crack images in damaged RC columns is obtained through the consideration of the generalized fractal dimensions. The authors have compiled a comprehensive database consisting of 445 crack maps obtained from cyclic experiments conducted on 110 rectangular RC column specimens exhibiting double‐curvature deformation mode. This database is utilized by the authors to develop and validate the proposed procedure. The research database contains a wide range of structural and geometric features. Five closed‐form equations are developed with the objective of estimating the peak IDR experienced by the RC columns during a seismic event. The predictive equations are derived through the utilization of symbolic regression technique, with the input parameters varying according to the availability of columns characteristic parameters. Results reveal that generalized fractal dimensions, especially D−1, are strong vision‐based indicator of damage in RC columns having correlation coefficients with IDR ranging from 0.82 to 0.92 across the considered plans. The seismic peak IDR obtained through the empirical equations can serve as the input engineering demand parameter (EDP) in the seismic loss estimation frameworks. This allows for the determination of the probability of exceeding damage states for structural and nonstructural components of concrete buildings. Finally, the practical implementation of the methodology is examined by its application to an actual case of a damaged column during the Kermanshah earthquake of magnitude 7.3 that occurred in 2017.

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Section: Plan Iii: Modeling By Two Gfds and Aspect Ratiomentioning

confidence: 99%

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confidence: 99%

Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns

Hamidia,

Jamshidian,

Afzali

et al. 2023

Structural Design Tall Build

Self Cite

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“…Researchers have studied methods to quantify the crack patterns on concrete/masonry shear walls [8][9][10][11][12][13], concrete columns [14][15][16][17], and operating bridges [18]. Ebrahimkhanlou et al [19,20] explored the application of fractal and multifractal analyses to assess crack patterns in concrete structures.…”

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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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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“…Reinaldo et al 22 combined linear analysis and limit design of threedimensional stress fields for the design of concrete structures. Mohammadjavad et al 23 proposed an image-based stiffness quantification method to estimate the postearthquake stiffness loss of RC structures. Ma et al 24 obtained the initial stiffness of the specimen through the interlayer displacement angle of the structure, and they calculated the design size of the steel plate by using the calculation formula of the thin-walled steel plate stiffness.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influence on structural seismic performance by the composite reinforcement method

Chu

et al. 2023

Structural Concrete

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This paper proposed three composite strengthening methods for repairing seismic‐damaged RC frames to restore their seismic capacity. Combined with the damage characteristics and failure mode of the frame under the earthquake action, the local strengthening of the column adopts the angle steel and gusset plate, and then the whole frame is strengthened by the inclined brace, the thin‐walled steel plate and the V‐shaped buckling restrained brace (BRB), respectively. Through the cyclic loading test, the main seismic indexes of the strengthened specimen, including hysteresis curve, skeleton curve, ductility, energy dissipation capacity, and stiffness degradation, are studied. The influence of different strengthening methods on the seismic performance of the seismic‐damaged specimens is analyzed. The results show that the seismic performance of strengthened specimens is significantly improved, and the bearing capacity, ductility, and energy dissipation capacity are increased by 2.58–5.57, 1.05–1.24, and 7.67–15.87 times, respectively. The thin‐walled steel plate shear wall and the inclined brace are destroyed at first during loading to effectively delay the premature destruction of the main frame. The composite strengthening method makes the original frame realize the failure mechanism of “strong joint weak member, strong column weak beam” under strong earthquake action. The strengthened specimen satisfies the seismic requirement and has good seismic performance. The seismic performance of the frame strengthened with the V‐shaped BRB is better than that of the other two strengthening methods. Based on the test date, the formula for calculating the shear‐bearing capacity of the beam end is presented.

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Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

Cited by 20 publications

References 78 publications

Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns

Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns

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

Analysis of the influence on structural seismic performance by the composite reinforcement method

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