Non-destructive Testing of CIPP Defects Using Machine Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU149220" target="_blank" >RIV/00216305:26110/23:PU149220 - isvavai.cz</a>
Result on the web
<a href="https://mater-tehnol.si/index.php/MatTech/article/view/1022/277" target="_blank" >https://mater-tehnol.si/index.php/MatTech/article/view/1022/277</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Non-destructive Testing of CIPP Defects Using Machine Learning Approach
Original language description
This paper compares different sensors used for IE proposed testing, namely piezoceramic and microphone sensors. It evaluates their ability to distinguish between defects present in the body of the CIPP via a machine-learning approach using random tree classifiers.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů