download0 view906
twitter facebook

공공누리This item is licensed Korea Open Government License

Title
Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest
Author(s)
Saeed Ullah정민중이우상
Publication Year
2018-10-15
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).
Keyword
nondestructive inspection; machine learning; dimensionality reduction; classification; ISOMAP; random forest
Journal Title
SENSORS
Citation Volume
18
ISSN
1424-8220
Files in This Item:
There are no files associated with this item.
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14799
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART94784633
Export
RIS (EndNote)
XLS (Excel)
XML

Browse