A Probability-Based Risk Assessment Model for the Sustainable Management of Urban Wastewater Collection Systems

Sewerage systems are among the most fundamental and indispensable components of urban infrastructure. However, inadequate management can result in malfunctions and subsequent rehabilitation processes, leading to various negative consequences. Identifying areas at high risk of failure and conducting system-based inspections can significantly improve the performance of sewer networks. This study identified and categorized 33 criteria that could cause sewer system failures: structural, operational, hydraulic and environmental defects. A Bayesian network (BN) model was developed to determine dependencies between the criteria, quantify uncertainty, investigate new information about the structural condition of assets and calculate the effects and sensitivities of the criteria on the probability of failure. A probability-based risk assessment model was then created using a fuzzy inference system (FIS) to predict risk levels in sewerage systems under different combinations of physical and operational conditions and hydraulic and environmental effects. A case study was performed on a sewer network in Malatya, Turkey, determining its failure probability to be 76.6%, placing it in the high-risk category. When the probability of pipe failure was set to 100% in the Bayesian network model to evaluate the relative influence of different criteria, the most influential factors were identified as flow velocity (74.8%), clogging (71.4%), and failure rate (71.1%). Thanks to the flexible structure of BNs, the proposed model is expected to be useful for performing risk analyses in systems involving uncertainty or missing data. It can also be used to prioritize rehabilitation, inspection and maintenance programs, improve infrastructure service quality and ensure system reliability in urban sewerage systems.