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.

中文翻译：

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弧长法提取裂纹特征

尽管在过去的几十年中，手动裂缝检查已广泛用于结构健康监测，但是计算机视觉方法的发展允许连续监测并弥补了人类判断的不准确性。在这项研究中，引入了一种基于图像的方法，称为“弧长法”，用于提取裂纹图案特征，包括裂纹宽度和裂纹长度。该方法包含两个主要步骤；第一步，在整个图像中估计裂纹区域。然后，该算法找到起点，遵循裂纹模式，并测量裂纹特征，例如裂纹宽度，裂纹长度和裂纹模式角度。通过在实验室在准静态循环载荷下测试的开裂结构混凝土剪力墙的一些案例研究，验证了该方法的有效性。案例研究表明，即使裂纹在两个或多个分支中传播，所提出的方法也能高效地遵循裂纹模式。该方法的应用在基础设施（例如混凝土桥梁和隧道）的裂缝监控中起着重要作用。