Non-destructive Testing of CIPP Defects Using Machine Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F24%3APU152171" target="_blank" >RIV/00216305:26110/24:PU152171 - isvavai.cz</a>

Výsledek na webu

<a href="https://mater-tehnol.si/index.php/MatTech/article/view/1000" target="_blank" >https://mater-tehnol.si/index.php/MatTech/article/view/1000</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17222/mit.2023.1000" target="_blank" >10.17222/mit.2023.1000</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-destructive Testing of CIPP Defects Using Machine Learning Approach

Popis výsledku v původním jazyce

In civil engineering, retrofitting actions involving repairs to pipes inside buildings and in extravehicular locations present complex and challenging tasks. Traditional repair procedures typically involve disassembling the surrounding structure, leading to technological pauses and potential work environment disruptions. An alternative approach to these procedures is using "cured-in-place pipes" (CIPP) technology for repairs. Unlike standard repairs, CIPP repairs do not require the disassembly of the surrounding structures; only the access points at the beginning and end of the pipe need to be accessible. However, this method introduces the possibility of different types of defects.1 This research aims to observe defects between the host and newly cured pipes. However, the presence of holes, cracks, or obstacles prevents attaining this desired close-fit state, ultimately reducing the life expectancy of the retrofitting action. This paper focuses on the non-destructive observation of these defects using the NDT Impact-Echo (IE) method. The study explicitly applies this method to CIPP composite pipe segments inside concrete host pipes, forming a testing polygon. Previous results have indicated that the mechanical behaviour of cured CIPP composite pipes can vary in stiffness depending on factors such as the curing procedure and environmental conditions.2 The change of acoustic parameters such as resonance frequency, attenuation and other features of typical IE signals can describe the stiffness evolution. 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.

Název v anglickém jazyce

Non-destructive Testing of CIPP Defects Using Machine Learning Approach

Popis výsledku anglicky

In civil engineering, retrofitting actions involving repairs to pipes inside buildings and in extravehicular locations present complex and challenging tasks. Traditional repair procedures typically involve disassembling the surrounding structure, leading to technological pauses and potential work environment disruptions. An alternative approach to these procedures is using "cured-in-place pipes" (CIPP) technology for repairs. Unlike standard repairs, CIPP repairs do not require the disassembly of the surrounding structures; only the access points at the beginning and end of the pipe need to be accessible. However, this method introduces the possibility of different types of defects.1 This research aims to observe defects between the host and newly cured pipes. However, the presence of holes, cracks, or obstacles prevents attaining this desired close-fit state, ultimately reducing the life expectancy of the retrofitting action. This paper focuses on the non-destructive observation of these defects using the NDT Impact-Echo (IE) method. The study explicitly applies this method to CIPP composite pipe segments inside concrete host pipes, forming a testing polygon. Previous results have indicated that the mechanical behaviour of cured CIPP composite pipes can vary in stiffness depending on factors such as the curing procedure and environmental conditions.2 The change of acoustic parameters such as resonance frequency, attenuation and other features of typical IE signals can describe the stiffness evolution. 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Materiali in tehnologije

ISSN

1580-2949

e-ISSN

1580-3414

Svazek periodika

58

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

SI - Slovinská republika

Počet stran výsledku

6

Strana od-do

13-17

Kód UT WoS článku

001339304000003

EID výsledku v databázi Scopus

2-s2.0-85207111355