Prediction of Span evaluation and Urgency assessment of Sewer pipes using Graph Neural Networks

Abstract

In recent years, aging sew erpipes have become aserious issue, with large-scale road collapses caused by pipe failures reported, resulting in damage that threatens human lives. To prevent the recurrence of such accidents, it is essential to accurately assess the condition of sewer pipes and promptly identify sections requiring repair. However, sewer pipe inspections involve evaluating a wide range of damage types and determining the condition and urgency for each pipe span. This task requires advanced expertise, and challenges such as variability in inspector evaluations and delays due to labor shortages persist. In this study, we developed a method to predict span evaluation and urgency assessment using a Graph Neural Network (GNN), based on inspection records from Kanazawa City. Input data included inspection target area, manhole-to-manhole length, pipe diameter, pipe type, and damage evaluation. The proposed method achieved high accuracy in predicting both span evaluation and urgency assessment.