Computer vision-based automated stiffness loss estimation for seismically damaged non-ductile reinforced concrete moment frames

Hamidia, Mohammadjavad; Ganjizadeh, Amirhossein

doi:10.1007/s10518-022-01408-w

Cited by 21 publications

(13 citation statements)

References 75 publications

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“…Note worthing that the main idea of the Box-counting method was previously published by the authors of the current study. 69 In the first step of the Box-counting method, the reciprocal variation of the input variables is provided in a 2D scatter plot; see the first phase of Figure 13. Next, various boxes with incremental dimensions are generated to cover the entire space of the scatter plot.…”

Section: Generating 3d Fragility Surfacementioning

confidence: 99%

“…For instance, the extent and complexity of crack patterns quantified through fractal theory have been widely used to assess structural integrity. [8][9][10]69,70 In particular, Hamidia et al used the fractal dimension of crack patterns to predict the peak drift ratio 11 of beam-column joints and strength deterioration 12 of nonductile concrete columns. In another study, Ebrahimkhalou et al 13 established a correlation-based link between the generalized fractal dimension of surface crack patterns to the structural performance of RCSWs.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several studies investigated image‐based damage features to find a strong indicator that is able to express the state of damaged structural components. For instance, the extent and complexity of crack patterns quantified through fractal theory have been widely used to assess structural integrity 8‐10,69,70 . In particular, Hamidia et al.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Generating 3d Fragility Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…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 . Based on the above‐mentioned literature, there is a gap of knowledge on the seismic peak drift ratio estimation using multifractal dimensions of crack patterns for non‐ductile RCMRF buildings.…”

Section: Introductionmentioning

confidence: 99%

“…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 Based on the above-mentioned literature, there is a gap of knowledge on the seismic peak drift ratio estimation using multifractal dimensions of crack patterns for non-ductile RCMRF buildings.…”

mentioning

confidence: 99%

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

Hamidia

Afzali

Jamshidian

et al. 2023

Structural Concrete

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The seismic damage to reinforced concrete (RC) components is conventionally quantified through various damage indices. In this paper, an image‐based procedure is developed for post‐earthquake residual stiffness quantification of RC columns. The proposed scenario‐based methodology is built upon the multifractal indices of the surface crack maps of the seismically damaged RC columns as the complexity measure for the images. An extensive databank from experiments on RC column specimens with a broad range of structural characteristics are collected and employed for generation and validation of the proposed procedure. Based on the availability of the structural information, five closed‐form regression‐aided equations are offered for estimating the stiffness loss in seismically damaged RC columns using predicting parameters among fractal indices, concrete strength, and the aspect ratio of the column. Results reveal that multifractal dimensions of crack maps are largely correlated with the stiffness degradation in RC columns. A sample specimen at various stiffness‐based damage indices is also presented as a case study to evaluate the predicted versus measured damage indices. The updated stiffness values acquired by the proposed equations can be utilized for stability evaluation, system identification, or further analysis of the seismically damaged RC buildings following an earthquake.

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Computer vision-based automated stiffness loss estimation for seismically damaged non-ductile reinforced concrete moment frames

Cited by 21 publications

References 75 publications

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

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

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

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

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