Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals
The structural integrity of bolts in civil structures is critical to ensuring safety and operational efficiency. Traditional visual inspection methods often fail to detect internal defects of installed bolts, necessitating more advanced approaches. On the other hand, ultrasonic testing which c...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-03-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30801 |
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| Summary: | The structural integrity of bolts in civil structures is
critical to ensuring safety and operational efficiency.
Traditional visual inspection methods often fail to detect
internal defects of installed bolts, necessitating more
advanced approaches. On the other hand, ultrasonic testing
which can detect internal defects is not as straightforward
to apply with signals difficult to analyse without the
relevant certified non-destructive testing personnel
qualifications and experience. This paper integrates
machine learning to automate ultrasonic testing signal
analysis in enhancing the reliability and precision of
defect detection of bolts. A-scan signals were analysed to
extract key features for machine learning models, with
Gradient Boosting Classifier emerging as the optimal model,
achieving an accuracy of 97% in defect classification. This
methodology targets bolt defects such as thinning and
corrosion, utilizing normalised signal features to
distinguish severity of defects while enabling automated
classification.
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| ISSN: | 1435-4934 |