Multidistribution Probabilistic Framework for Stormwater Pipeline Risk Assessment and Capital Planning

Stormwater pipeline deterioration poses a significant challenge for municipalities. Traditional linear life-scoring methods often overestimate midlife failure risk by assuming uniform deterioration rates. This can misdirect limited capital funds. Our study introduces a probabilistic framework that uses Weibull, lognormal, and normal distributions to better represent distinct pipe failure patterns. This framework integrates conditional failure probabilities with consequence assessments to generate risk scores. These scores are linked to inflation-adjusted replacement costs for multiyear budget planning and prioritization. We applied this methodology in Sugar Land, Texas, analyzing 24,863 stormwater assets across 463 mi of pipeline. Statistical tests showed the probabilistic models produced more realistic results than the linear approach (p<0.001). The linear method overestimated failure probabilities by 3 to 10 times. Because local failure records were unavailable, we drew distribution parameters from published studies and manufacturer data, providing a practical approach for data-scarce utilities. Weibull distributions best described deterioration in reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Lognormal distributions provided the best fit for polyvinyl chloride (PVC) pipelines. The framework identified a USD 181 million infrastructure backlog, contrasting sharply with a USD 5 million annual funding budget. System-level simulations showed that current funding plateaus risk reduction after two years. Conversely, annual programs of USD 8 to USD 10 million reduce aggregate system risk by over 50 percent within a decade. Sensitivity analysis confirmed that backlog costs and policy violation rates remained stable under different assumptions for missing installation years. This demonstrates the framework’s reliability even with incomplete data. Overall, this approach provides utilities with a practical, statistically grounded method for prioritizing stormwater pipe renewal when local failure data is limited. It enhances both technical credibility and financial accountability in asset management planning.