Non-destructive Testing of CIPP Defects Using Machine Learning Approach - Dvořák, Richard; Pazdera, Luboš; Topolář, Libor; Jakubka, Luboš; Puchýř, Jan (Institute of Metals and Technology, 2024)
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 uses the cured-in-place-pipe (CIPP) technology for repairs. Unlike standard repairs, CIPP repairs do not require a 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 1 This research aims to observe the defects between the host and newly cured pipes. The presence of holes, cracks, or obstacles prevents achieving a desired close-fit state, ultimately reducing the life expectancy of the retrofitting. This paper focuses on the non-destructive observation of these defects using the non-destructive testing (NDT) impact-echo (IE) method. The study explicitly applies this method to the composite segments inside concrete host pipes, forming a testing polygon. Previous results have indicated that the mechanical behaviour of cured composite pipes can vary in stiffness depending on factors such as the curing procedure and environmental conditions.2 2 The change in acoustic parameters such as resonance frequency, attenuation and other features of typical IE signals can describe the stiffness evolution. This study compares different sensors used for the proposed IE testing, namely piezoceramic and microphone sensors. It evaluates their ability to distinguish between the defects present in the body of a CIPP via a machine-learning approach using random tree classifiers.

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 uses the cured-in-place-pipe (CIPP) technology for repairs. Unlike standard repairs, CIPP repairs do not require a 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 1 This research aims to observe the defects between the host and newly cured pipes. The presence of holes, cracks, or obstacles prevents achieving a desired close-fit state, ultimately reducing the life expectancy of the retrofitting. This paper focuses on the non-destructive observation of these defects using the non-destructive testing (NDT) impact-echo (IE) method. The study explicitly applies this method to the composite segments inside concrete host pipes, forming a testing polygon. Previous results have indicated that the mechanical behaviour of cured composite pipes can vary in stiffness depending on factors such as the curing procedure and environmental conditions.2 2 The change in acoustic parameters such as resonance frequency, attenuation and other features of typical IE signals can describe the stiffness evolution. This study compares different sensors used for the proposed IE testing, namely piezoceramic and microphone sensors. It evaluates their ability to distinguish between the defects present in the body of a CIPP via a machine-learning approach using random tree classifiers.
Non-destructive Testing of CIPP Defects Using Machine Learning Approach
Keywords: Retrofitting; Cured-in-Place Pipes; Non-Destructive Testing; Impact-Echo Method; Pipe Defects; Acoustic Parameters; Machine Learning; Classification.; Retrofitting; Cured-in-Place Pipes; Non-Destructive Testing; Impact-Echo Method; Pipe Defects; Acoustic Parameters; Machine Learning; Classification.