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

Abstract

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.

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

“…This method of dividing the databank into training and testing datasets is commonly used to develop and validate predictive models. 24,34,54,[92][93][94][95] 7.1 | Plan I: modeling by D 0 and aspect ratio…”

“…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.…”

“…In other words, the testing dataset was not utilized in the process of developing the equations. This method of dividing the databank into training and testing datasets is commonly used to develop and validate predictive models 24,34,54,92–95 …”