Aging infrastructure worldwide has led to an interest in using innovative solutions for non-destructive damage assessment. The traditional in-person assessment of large-scale infrastructure like dams and bridges etc. can be very time and resource consuming. With advances in technology, image processing and machine learning have shown promise in providing alternate ways of damage assessment in large-scale and difficult-to-access infrastructure. However, this approach has mostly been applied passively and not in real-time. The work presented here describes a supervised machine learning based approach to damage analysis of concrete structures. Python programming language is used to write and train algorithms to provide a real-time damage analysis. In addition to crack detection, the dimensional analysis provides additional information regarding an existing or developing crack. With this type of real-time information, timely action can be taken regarding decisions like performing repairs or decommissioning a structure for public safety.
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