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Non-destructive damage detection for steel pipe scaffolds using MFL-based 3D defect visualization

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Abstract

Magnetic flux leakage (MFL) is a commonly used non-destructive technique for diagnosing defects in steel structures. In this study, we investigate a magnetic flux leakage (MFL)-based 3D visualization approach for identifying metal loss defects and evaluating their severity, which is a major concern in various industry fields where the structural integrity of specimens is crucial. We focus specifically on small-dimensional steel pipelines and steel pipe scaffolds as target specimens, which require a consistent inspection system to maintain their healthy performance for their intended purposes. When a magnetic field encounters a small air gap created by a defect, it spreads out because the air cannot support as much magnetic field per unit volume as a magnet can. We detect and measure the MFL signal that leaks due to the metal loss using a sensor head prototype containing hall sensors, which is used to scan the steel pipeline. The measured MFL signals are used to investigate abnormalities in the targeted pipeline specimen. Once the MFL signal is collected and measured, a series of digital signal processing steps are performed to enhance the resolution and extent of the defect level. This study proposes a 3D visualization of the defect profile based on the time domain MFL data, which provides insights into how the defect occurs on the target specimen and the general location of the defect. This visualization approach offers an effective means of evaluating the severity of metal loss defects in steel structures and can help ensure the long-term structural integrity of these specimens.

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Owing to the sensitive nature of our research, we are unable to publicly disclose the data. However, interested parties are welcome to reach out to the authors directly for further information.

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Acknowledgements

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) and Korea Ministry of Land, Infrastructure and Transport (MOLIT) as Innovative Talent Education Program for Smart City. And grant funded by the Ministry of Land, Infrastructure and Transport (National Research for Smart Construction Technology: Grant RS-2020-KA156488), National Research Foundation of Korea (NRF) through the Ministry of Science and ICT (MSIT), Korea Government, under Grant NRF-2021R1A4A3033128.

Funding

This research was supported by Ministry of Land, Infrastructure and Transport (National Research for Smart Construction Technology, 22SMIP-A156492-03, Seunghee Park).

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Authors and Affiliations

  1. Department of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon-Si, Korea

    Fitsum Emagnenehe Yigzew, Hansun Kim & Seunghee Park

  2. Department of Convergence Engineering for Future City, Sungkyunkwan University, Suwon-Si, Korea

    Sebum Mun

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  1. Fitsum Emagnenehe Yigzew
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  2. Hansun Kim
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  4. Seunghee Park
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Correspondence to Seunghee Park.

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Yigzew, F.E., Kim, H., Mun, S. et al. Non-destructive damage detection for steel pipe scaffolds using MFL-based 3D defect visualization. J Civil Struct Health Monit 14, 501–509 (2024). https://doi.org/10.1007/s13349-023-00726-0

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