Publication detail

Non-Destructive Characterization of Cured-in-Place Pipe Defects

DVOŘÁK, R. JAKUBKA, L. TOPOLÁŘ, L. RABENDA, M. WIROWSKI, A. PUCHÝŘ, J. KUSÁK, I. PAZDERA, L.

Original Title

Non-Destructive Characterization of Cured-in-Place Pipe Defects

Type

journal article in Web of Science

Language

English

Original Abstract

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

Keywords

non-destructive testing; machine learning; retrofitting; cured-in-place pipes; polymers; pipe defects

Authors

DVOŘÁK, R.; JAKUBKA, L.; TOPOLÁŘ, L.; RABENDA, M.; WIROWSKI, A.; PUCHÝŘ, J.; KUSÁK, I.; PAZDERA, L.

Released

8. 12. 2023

Publisher

MDPI

Location

Basel, Switzerland

ISBN

1996-1944

Periodical

Materials

Year of study

16

Number

24

State

Swiss Confederation

Pages from

1

Pages to

31

Pages count

31

URL

https://www.mdpi.com/1996-1944/16/24/7570

Full text in the Digital Library

http://hdl.handle.net/11012/244291

BibTex

@article{BUT185707,
  author="Richard {Dvořák} and Luboš {Jakubka} and Libor {Topolář} and Martyna {Rabenda} and Artur {Wirowski} and Jan {Puchýř} and Ivo {Kusák} and Luboš {Pazdera}",
  title="Non-Destructive Characterization of Cured-in-Place Pipe Defects",
  journal="Materials",
  year="2023",
  volume="16",
  number="24",
  pages="1--31",
  doi="10.3390/ma16247570",
  issn="1996-1944",
  url="https://www.mdpi.com/1996-1944/16/24/7570"
}

Documents

materials-16-07570-v2.pdf