Development of a Risk Modeling Framework for Water Pipeline Renewal Prioritization

The water pipeline infrastructure in the US is aging, posing an economic burden on water utilities. The improvement in the areas of data collection technologies (new sensory technologies and open-source federal databases), modeling techniques, and computational capabilities have enabled water utilities to adopt advanced asset management techniques to alleviate this burden. Risk-based asset management helps water utilities use data-driven techniques to prioritize water pipelines for renewal decision making (repair, rehabilitation, and replacement). The current practices include using quantitative index-based methods and semi-quantitative matrix-based visualizations. This study found that water utilities have investigated and developed techniques for modeling the likelihood of failure (LoF) and consequence of failure (CoF), the critical functions needed for risk analysis. However, the development of such models has not translated into better risk-based decision support systems for water utilities, as they still use limited aspects of their risk analyses’ results to prioritize pipes for renewal. The authors also found that there are significant areas of improvement in the current methodology for risk matrices like removing risk ties, incorporating game-theoretic criteria like risk attitudes, poor resolution for risk categories for prioritization action, and resource allocation, among others. This study shows how risk analysis methodologies for water pipeline infrastructure could be improved based on current scientific paradigms in risk modeling, including game-theoretic approaches. The proposed improvements can help water utilities improve the resolution, accuracy, and categorization of water pipes in their risk models, improving the reliability and usefulness of risk models for water pipeline renewal prioritization.