Grant

• 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.
• Create a practical, risk-based framework to rank critical assets for maintenance, rehabilitation, and capital investment planning.
• Define minimum data requirements needed to perform reliable risk-based prioritization.

WRF RFP Contact: Jian Zhang, PhD, PE

For instructions on how to submit proposals, refer to these instructions or this video.

The Potential Participants listed at the bottom of this page have indicated interest in participating in this research. This information is updated frequently as utilities are encouraged to volunteer throughout the RFP cycle.