Real-time surface damage analysis of concrete structures using Python-based image analysis

Shivam Jha; Amardeep Kaur

doi:10.1117/12.2657553

18 April 2023 Real-time surface damage analysis of concrete structures using Python-based image analysis

Shivam Jha, Amardeep Kaur

Proceedings Volume 12487, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII; 1248717 (2023) https://doi.org/10.1117/12.2657553
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Abstract

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.

Citation Download Citation

Shivam Jha and Amardeep Kaur "Real-time surface damage analysis of concrete structures using Python-based image analysis", Proc. SPIE 12487, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII, 1248717 (18 April 2023); https://doi.org/10.1117/12.2657553

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KEYWORDS

Image segmentation

Image analysis

Education and training

Algorithm development

Edge detection

Image processing

Machine learning

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