Author(s): Inhee Yeo; Jun Lee; Jihoon Choi; Soojin Moon

Linked Author(s): INHEE YEO, JIHOON CHOI

Keywords: Rtificial Intelligence (AI) Sewer pipe Defect detection Deterioration Automatic reporting

Abstract: Frequent ground settlement (sinkhole) incidents due to the deterioration of sewer pipes have led to a steady increase in the demand for sewer pipe diagnostic projects aimed at preventing safety accidents by proactively monitoring pipeline deterioration and defect degree in advance. Accordingly this study aims to improve the efficiency of condition assessment and diagnostic processes for systematic sewer pipe maintenance through the development of core modules namely, sewer pipe condition assessment, AI-based defect detection, and automated diagnostic result reporting within an intelligent (AI) sewer pipe condition assessment system. The deterioration rate and residual lifespan assessment module, along with the AI-based defect detection module developed in this study, enables more effective monitoring of sewer pipe conditions and provides intuitive insights into optimal timing for rehabilitation or replacement. In addition, the system includes functionality to automatically generate defect detection results in a predefined report format, making the traditionally subjective diagnostic approach more objective and enhancing work efficiency. Future research will focus on establishing an integrated sewer pipe asset management platform based on this system to support decision-making in sewer pipe maintenance and exploring applications for asset management of other infrastructure facilities.

DOI:

Year: 2025