Risk-Based Prioritization of Critical Valves Using Limited Utility Data and Machine Learning (Request for Proposals)

Description: Risk-based prioritization of critical valves using limited utility data and machine learning. Project objectives: develop a data-efficient machine learning model to estimate likelihood of failure (LOF) of valves using commonly available utility data, including asset attributes, condition indicators, environmental factors, and geohazards; develop consequence of failure (COF) analysis based on hydraulic modeling and network connectivity to quantify service disruption, affected population, outage duration, and community impacts resulting from valve inoperability or failure; incorporate uncertainty in asset condition and operability into risk estimation and prioritization. See attached file.