Arc Length method for extracting crack pattern characteristics

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.

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

“…The authors previously extended the Arc Length method to measure the main characteristics of surface cracking, including cracking length, cracking width, and pattern inclination. 7 Herein a brief description of the Arc length method is presented. The architecture of the Arc length algorithm generally contains two parts: finding the initiation of crack patterns and capturing the flow of crack patterns.…”

“…For instance, Xiaodong et al 6 employed a filter-based algorithm to measure crack patterns from images of RC specimens. Asjodi et al 7 also used image processing filtering to measure the prominent characteristics of crack patterns, including the crack width, length, and pattern inclination. 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.…”

Long‐lasting phosphorescent microcapsules based on in‐situ polymerization method for damage sensing

“…Even minor damages, after continuous development, will adversely affect the remaining life of the structure and even lead to catastrophic accidents 3 . Therefore, it is not only necessary to strictly control the entire process in the structural design and construction but also to monitor the operation stage of the built buildings 4 . The full cycle assessment of their health is conducted to ensure the safety of the structure 5 .…”

“…3 Therefore, it is not only necessary to strictly control the entire process in the structural design and construction but also to monitor the operation stage of the built buildings. 4 The full cycle assessment of their health is conducted to ensure the safety of the structure. 5 The current mainstream structural health monitoring system is usually realized by a set of systems working together, consisting of electronic sensors, related algorithm programs and data processing equipment.…”

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

“…[15][16][17] Convolutional neural network (CNN) is the novel approach for this purpose. [18][19][20][21] As another example in this field, Asjodi et al 22 developed arc length method for extracting crack pattern characteristics, such as crack length and crack width for the image of damaged reinforced concrete element.…”