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A Preliminary Study on Leakage Detection of Deteriorated Underground Sewer Pipes Using Aerial Thermal Imaging

Authors :
Sung-Yong Park
Jihuan Jin
Yong-Seong Kim
Bibek Tamang
Songsik Park
Seungjoo Lee
Hyun-Taek Lim
Source :
International Journal of Civil Engineering. 18:1167-1178
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

Due to less visibility, underground facilities such as sewer pipelines are subjected to degradation until a major failure occurs. So, leakage detection of deteriorated underground sewer pipes is necessary to prevent expensive rehabilitation costs. The major causes of the frequent occurrence of ground subsidence and sewer leakage are excessive use of groundwater, reckless exploitation of natural resources, etc. In this study, the aerial thermal imaging (ATI) technique has been used to identify the leakage points in sewer pipelines by analyzing surface thermal diffusion behavior of the ground surface. Leakage is suspected at points of anomalous temperature variation. Then, ground-penetrating radar and closed-circuit television surveys have been carried out to strengthen the suspicion of sewer leakage. Irregular, erratic parabolic-shaped lines are observed at the points where the leakage is suspected through ATI. Finally, the test-pit excavation and simple cone penetration tests have been carried out which confirms the sewer leakage points. The leakage points are confirmed by a greater depth of penetration. The results of the cone penetration test carried out in E-21 and 23-24 sewer pipelines show that the depth of penetration at the leakage points is about 35 cm, which is greater than the depth of penetration at other points. So, it can be concluded that the aerial thermal imaging technique is an effective method to predict leakage in deteriorated underground sewer pipelines.

Details

ISSN :
23833874 and 17350522
Volume :
18
Database :
OpenAIRE
Journal :
International Journal of Civil Engineering
Accession number :
edsair.doi...........16084805b8d98c9bff32311b73dec4ed