Strategic Remote-Operated Valve Placement in Looped Water Transmission Networks: Risk-Based and Hydraulic Impact–Driven Approach

Timely isolation of burst locations in water transmission systems is critical for mitigating extensive water loss (WL) and minimizing disruptions in water supply. However, delays in postfailure action time such as decision-making and manual valve operations can lead to substantial infrastructure damage and prolonged service interruption. This study proposes a novel, field-informed framework that integrates machine learning, graph theory, and pressure-driven hydraulic simulation to quantify the benefits of retrofitting existing valves with remote operation and prioritizes installation location. A machine learning–based probability of failure (PoF) model, trained on long-term asset data, is used to simulate realistic burst scenarios. For each failure case, optimal isolation and interconnection valve sets are identified through graph-based analysis combined with hydraulic validation. Field-based manual operation delays are estimated using the Open-Source Routing Machine (OSRM), incorporating actual road network data to reflect true response times. The performance of remote-operated valves (ROVs) is assessed using two quantitative indicators: WL (as a proxy for cascading indirect impacts) and water supply shortage (WSS; direct impact). By comparing manual and ROV-enabled responses, the framework identifies the optimal ROV deployment for each segment, depending on specific operational conditions such as redundancy and supply availability. The analysis shows how the effectiveness of ROVs varies depending on network resilience, as well as how ranking valve sets by marginal benefits can support efficient investment decisions. Overall, this approach enhances burst response effectiveness by integrating proactive planning with postfailure interventions and supports practical decision-making in complex transmission systems.