A critical review of the application of machine intelligence methods for the risk assessment of structural cracks
Kinny Garg, Rajeev Kamal Sharma, Preeti Sharma
Abstract. Cracks pose a serious threat to a structure’s safe and dependable operation. It is necessary to detect and assess the surface cracks properly and quickly in order to preserve a structure’s serviceability and safety. Various methods have been proposed to improve the accuracy and efficiency of risky crack detection while adjusting to the detecting environment, such as image and sensor detection. This study offers a systematic overview of the practices related to the development and application of modern machine learning crack detection methods for the purpose of identifying, localizing, and quantifying fractures in concrete dams. It focuses on methods of acquiring images paired with algorithms for digital image processing. Being non-destructive, non-contact, extremely effective, and broadly applicable are only a few of its many advantages. To facilitate future research, this paper’s conclusion looks at research difficulties and future directions regarding the field of crack detection technologies. Image processing techniques can be used on scanned or taken photographs of the infrastructure components to find any potential flaws. To improve performance results and reliability in risky crack detection, machine learning techniques are being used more and more in addition to image processing. An overview of image-based crack detection methods utilizing machine learning or image processing is given in this paper.
Keywords
Image Execution, Computational Intelligence, Detection of Cracks
Published online 3/1/2025, 5 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Kinny Garg, Rajeev Kamal Sharma, Preeti Sharma, A critical review of the application of machine intelligence methods for the risk assessment of structural cracks, Materials Research Proceedings, Vol. 49, pp 281-285, 2025
DOI: https://doi.org/10.21741/9781644903438-28
The article was published as article 28 of the book Mechanical Engineering for Sustainable Development
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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