Optimizing Maintenance Schedules for Wastewater Pipes Using Physics-Based Machine Learning
Keywords:
Failure risk assessment, Machine learning (ML), Maintenance optimization (MO), Physics-based modeling, Pipe deterioration prediction, Proactive asset management, Wastewater infrastructureAbstract
Aging wastewater infrastructure poses significant environmental and public health risks, especially when maintenance is reactive rather than proactive. This paper presents a hybrid framework that combines physics-based modeling with Machine Learning (ML) to optimize maintenance schedules for wastewater pipelines. We integrate pipe deterioration models based on material properties, environmental stressors, and flow dynamics with supervised learning models trained on historical inspection and failure data. The proposed approach predicts failure likelihood with higher accuracy than traditional statistical models, enabling cost-effective, risk-informed maintenance scheduling. A case study using data from a mid-sized U.S. city demonstrates the model’s effectiveness, reducing anticipated failures by 32% and maintenance costs by 18% over a 10-year planning horizon.
