Peak drift ratio estimation for RC moment frames using multifractal dimensions of surface crack patterns

Hamidia, Mohammadjavad; Ganjizadeh, Amirhossein; Dolatshahi, Kiarash M.

doi:10.1016/j.engstruct.2022.113893

Cited by 20 publications

(18 citation statements)

References 39 publications

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“…In this paper, the box sizes are assumed to be the 1, 1/2, 1/4, 1/8, 1/16, and 1/32 times the size of the column‐joint region, and the width‐to‐length ratios of boxes are set to be equal to the aspect ratio of the corresponding column‐joint region. Such assumption is consistent with the similar studies on obtaining the multifractal dimensions of the reinforced concrete elements in the literature 33,35–40,68–71 . In addition, the sensitivity of the column‐joint region multifractal dimensions to the box sizes is investigated for the specimen BS‐OL 42 (Figure 1e) at drift ratio 2.9%.…”

Section: Multifractal Dimensionsupporting

confidence: 88%

“…Such procedure has been followed in the previous studies for obtaining the fractal dimensions. 33,34,[36][37][38][39][40][63][64][65][66][67][68] To this end, a scaling factor of 20 pixels for 1 cm was considered, and the cracks were digitized using a line with a one pixel thickness. Noteworthy to mention that according to Equation 2, the scaling factor of centimeter to pixels used for regenerating the images does not affect the multifractal dimensions values.…”

Section: Multifractal Dimensionmentioning

confidence: 99%

“…Such assumption is consistent with the similar studies on obtaining the multifractal dimensions of the reinforced concrete elements in the literature. 33,[35][36][37][38][39][40][68][69][70][71] In addition, the sensitivity of the column-joint region multifractal dimensions to the box sizes is investigated for the specimen BS-OL 42 (Figure 1e) at drift ratio 2.9%. Figures 3a, 4a, and 5a show the multifractal dimensions obtained using box sizes equal to 1, 1/2, 1/4, 1/8, 1/16, and 1/32 times the size of the columnjoint region for values of q equal to À1, 0, and +1, respectively.…”

Section: Multifractal Dimensionmentioning

confidence: 99%

“…All the crack patterns from the images of the database were regenerated manually by drawing in a binary format (black and white). Such procedure has been followed in the previous studies for obtaining the fractal dimensions 33,34,36–40,63–68 . To this end, a scaling factor of 20 pixels for 1 cm was considered, and the cracks were digitized using a line with a one pixel thickness.…”

Section: Multifractal Dimensionmentioning

confidence: 99%

“…Madani and Dolatashahi 39 later proposed predictive equations to estimate the strength and stiffness loss in shear walls. Recently, Hamidia et al 40 developed a methodology for obtaining seismic peak drift ratio of RCMRFs using multifractal dimensions of beams, columns and joint segments crack patterns. The residual stiffness estimation using multifratcal dimensions of surface crack patterns for non‐ductile RCMRFs was the focus of another study by Hamidia and Ganjizadeh 41 .…”

Section: Introductionmentioning

confidence: 99%

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Post‐earthquake damage evaluation of non‐ductile RC moment frames using surface crack patterns

Hamidia

Ganjizadeh

2022

Structural Contr & Hlth

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In this paper, a methodology is developed for seismic damage identification of non-ductile reinforced concrete moment frames using surface crack images. A comprehensive database comprising 264 surface crack patterns from quasistatic test results of 61 non-ductile beam-column joint specimens at various damage levels is collected. The database covers a broad range of structural and geometric properties. Multifractal dimensions of the column and joint regions of damaged beam-column subassembly images are obtained and used as the complexity and irregularity index of the surface crack patterns. Predictive equations are developed that estimate the peak drift ratio during an earthquake using the multifractal dimensions of surface crack patterns. Six scenarios are considered for obtaining the predictive equations. The developed empirical equations rely on the multifractal dimensions along with the structural properties of the non-ductile beam-column joint. The peak drift ratio estimated by the proposed methodology can finally be used as the input engineering demand parameter to the existing fragility curves in the literature to identify the probability of exceedance from damage states of non-ductile reinforced concrete moment frames.

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Section: Multifractal Dimensionsupporting

confidence: 88%

Section: Multifractal Dimensionmentioning

confidence: 99%

Section: Multifractal Dimensionmentioning

confidence: 99%

Section: Multifractal Dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post‐earthquake damage evaluation of non‐ductile RC moment frames using surface crack patterns

Hamidia

Ganjizadeh

2022

Structural Contr & Hlth

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Three‐dimensional fragility surface for reinforced concrete shear walls using image‐based damage features

Asjodi

Dolatshahi

Burton

2023

Earthq Engng Struct Dyn

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This paper proposes a new data-driven method to generate three-dimensional fragility surfaces for post-earthquake damage assessment of reinforced concrete (RC) shear walls (SWs) using image-based damage features. A research database comprised of 212 images corresponding to 66 damaged reinforced concrete shear walls tested under quasi-static cyclic loads is utilized. The walls are categorized into three damage states defined based on different load points along the backbone curve. Convolutional kernel-based filters are then employed to measure the crack patterns and crushed areas from images of the damaged walls. A set of 360,000 sub-cracks from the 212 images is analyzed using Gaussian mixture modeling to distinguish between shear and flexural cracking. These two types of cracking, along with crushing, are the prominent characteristics used to determine the damage states of the RCSWs. The extracted features of shear and flexural cracking are also compressed into a unique cracking index using the Principal Component Analysis theory for dimensionality reduction. Using the compressed feature of cracking and crushing, a new methodology for generating three-dimensional fragility surfaces, entitled the Box-counting method, is introduced, and the damage surfaces are developed based on visual damage features. The damage surfaces are finally formulated using statistical models or machine learning regression learners. The proposed fragility surface can be used for postearthquake damage state identification, risk assessment, and loss estimation of damaged RCSWs.

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Evaluation of strength and stiffness degradation of RC shear walls: An integrated image processing and deep learning approach

Ji,

Yu,

Gao

et al. 2024

Earthq Engng Struct Dyn

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In the aftermath of an earthquake, damage detection and performance evaluation of structural components are imperative for assessing the residual seismic capacity of a building. In this study, an integrated image processing and deep learning approach was developed to evaluate the degradation in strength and stiffness (i.e., strength reduction and stiffness reduction) of reinforced concrete (RC) shear walls. The approach comprised two main tasks: detecting and localizing visible seismic damage from photographs and evaluating strength and stiffness degradation based on this information. The semantic segmentation network, Damage‐Net, was used for damage detection and localization. A novel crack morphological processing layer and a patch feature extraction layer were developed for damage feature extraction and compression. A lightweight deep convolutional neural network named DegradeEval‐Net_v2, featuring the upgraded dilated and separable convolution block and multi‐layer perception, was developed to link the damage feature with strength and stiffness degradation. A database comprising test data and photographs of 14 RC shear wall specimens with a flexural‐dominated behavior mode and high to intermediate ductility was constructed to train and test the DegradeEval‐Net_v2 network. The results indicate that DegradeEval‐Net_v2 substantially improved the performance assessment accuracy of damaged RC shear walls, with a 35% smaller root mean square error (RMSE) for stiffness degradation evaluation and 75% smaller RMSE for strength degradation evaluation, compared with the provisions specified in JBDPA and FEMA guidelines. Moreover, evaluation results on test sets demonstrate that introducing the damage feature extraction and compression layers effectively preserved local crack information and improved the accuracy with which stiffness reduction was evaluated. In addition, DegradeEval‐Net_v2 outperformed ResNet18 and MobileNet V3 in terms of balanced efficiency and accuracy. Interpretability analysis demonstrates that the model learned the distinct contribution patterns of various visible damage indexes to stiffness and strength degradation across different loading levels.

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Peak drift ratio estimation for RC moment frames using multifractal dimensions of surface crack patterns

Cited by 20 publications

References 39 publications

Post‐earthquake damage evaluation of non‐ductile RC moment frames using surface crack patterns

Post‐earthquake damage evaluation of non‐ductile RC moment frames using surface crack patterns

Three‐dimensional fragility surface for reinforced concrete shear walls using image‐based damage features

Evaluation of strength and stiffness degradation of RC shear walls: An integrated image processing and deep learning approach

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