No abstract

Although manual crack inspection has been widely used for structural health monitoring over the last decades, the development of computer vision methods allows continuous monitoring and compensates the human judgment inaccuracy. In this study, an image-based method entitled Arc Length method is introduced for extracting crack pattern characteristics, including crack width and crack length. The method contains two major steps; in the first step, the crack zones are estimated in the whole image. Afterwards, the algorithm finds the start point, follows the crack pattern, and measures the crack features, such as crack width, crack length, and crack pattern angle. The efficiency of the method is validated using a few case studies from cracked structural concrete shear walls tested in the laboratory under quasi-static cyclic loadings. The case studies show high efficiency of the proposed method in following the crack patterns even when the crack propagates in two or more branches. The application of this approach plays a significant role in crack monitoring of infrastructures, such as concrete bridges and tunnels.

No abstract

No abstract

No 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.

No abstract

No abstract