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공공누리This item is licensed Korea Open Government License

dc.contributor.author
Saeed Ullah
dc.contributor.author
정민중
dc.contributor.author
이우상
dc.date.accessioned
2019-08-28T07:42:20Z
dc.date.available
2019-08-28T07:42:20Z
dc.date.issued
2018-10-15
dc.identifier.issn
1424-8220
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14799
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART94784633
dc.description.abstract
Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).
dc.language
eng
dc.relation.ispartofseries
SENSORS
dc.title
Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest
dc.citation.endPage
11
dc.citation.number
10
dc.citation.startPage
1
dc.citation.volume
18
dc.subject.keyword
nondestructive inspection
dc.subject.keyword
machine learning
dc.subject.keyword
dimensionality reduction
dc.subject.keyword
classification
dc.subject.keyword
ISOMAP
dc.subject.keyword
random forest
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7. KISTI 연구성과 > 학술지 발표논문
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