AI-Assisted Failure Risk Screening for Urban Sewer Networks Based on Operational Utility Records

Introduction: Urban sewer networks are essential elements of environmental infrastructure, but their failures may cause wastewater leakage, infiltration, blockages, local flooding and environmental contamination. Recent developments in artificial intelligence and machine learning create new possibilities for supporting predictive maintenance and prioritising sewer inspections. The study proposes an AI-assisted approach for preliminary failure risk screening in an urban sewer network using operational records from a municipal utility.

Methods: The analysis was based on historical sewer failure records, including information on failure occurrence, location, pipe characteristics and reported causes. The dataset was organised into explanatory variables suitable for data-driven assessment, such as pipe diameter, material, age group, failure type, seasonal occurrence and repair-related information. Descriptive statistics and failure intensity indicators were combined with a machine-learning-oriented workflow for identifying sewer sections with increased susceptibility to operational problems. The proposed framework was designed as a transparent decision-support tool rather than a fully automated replacement for engineering assessment.

Results: The developed approach enables the classification of sewer network elements into priority groups for inspection and maintenance. It also supports the identification of recurring operational patterns associated with blockages, leakage, infiltration or structural deterioration. The AI-assisted screening concept may improve the use of incomplete utility records by transforming them into practical information for maintenance planning.

Conclusion: The study shows that operational sewer failure records can be used as a basis for AI-supported asset management. The proposed framework may help municipal utilities move from reactive maintenance towards more preventive and risk-based sewer network management.