Sueyeun Oak

Flushing planning requires utilities to prioritize pipes and service blocks using heterogeneous structural and hydraulic information. This study proposes a data envelopment analysis (DEA)-assisted framework that links pipe-level screening, service-block prioritization, and candidate flushing-segment planning. The framework separately evaluates sedimentation and detachment conditions using input-oriented Banker–Charnes–Cooper (BCC) models based on seven indicators, while avoiding the need to prescribe a common expert-defined weighting vector. The framework was applied to a real water distribution system with 481 pipes and 394 junctions, identifying 88 sedimentation-priority pipes and 12 detachment-priority pipes. Length-weighted aggregation within the existing block structure ranked Blocks 1, 7, 2, and 4 highest, whereas length-normalized analysis identified Block 2 as having the greatest concentration of priority pipes. The leading four-block group remained unchanged across the tested pipe-length exponents, and the top-12 detachment set was retained when the velocity-difference screening level was varied from 0.5 to 2.0 m/s. Additional model-form, preprocessing, indicator-omission, and equal-weight comparisons were used to characterize the sensitivity of pipe-level priorities. Finally, existing valve and hydrant locations were used to delineate five candidate flushing segments in a representative block. The framework provides a systematic basis for directing detailed hydraulic verification and field implementation.

Emergency interconnected operation (EIO) between adjacent blocks in a water distribution system (WDS) is one of the most effective countermeasures of a water utility when abnormal conditions (e.g., suspended water supply, pipe burst) occur at a certain location. For improved WDS operation and management, calculating a reliable performance indicator that accurately represents the states of consumers under both normal and abnormal conditions is essential. In this study, a water supply performance indicator (WSPI) was developed for assessing the hydraulic performance of a WDS in terms of consumer usability and satisfaction. A fuzzy function is used to represent the consumer satisfaction with the water supply level, and the satisfaction of different consumers can be adjusted by setting the variables of the fuzzy function. The WSPI can be applied to a hydraulic analysis model based on data from an actual WDS to assess the water supply capacity for each node and the entire network. It can also be used in an advanced pressure-driven analysis model to assess the WDS performance under various abnormal conditions. The proposed WSPI was applied to six suspended water supply scenarios of an actual WDS with and without EIO to assess the effectiveness of this countermeasure.

Understanding the impact and duration (consequences) of different component failures (cause) in a water supply and distribution system (WSDS) is a critical task for water utilities to develop effective preparation and response plans. During the last three decades, few efforts have been devoted to developing a visualization tool to display the relationship between the failure cause and its consequences. This study proposes two visualization methods to effectively show the relationship between the two failure entities: A failure cause–impact–duration (CID) plot, and a bubble plot. The former is drawn for an effective snapshot on the range (extent) of failure duration and the impact of different failures, whereas the latter provides failure frequency information. A simple and practical failure classification system is also introduced for producing the two proposed plots effectively. To verify the visualization schemes, we collected records of 331 WSDS component failures that occurred in South Korea between 1980 and 2018. Results showed that (1) the proposed CID plot can serve as a useful tool for identifying most minor and major WSDS failures, and (2) the proposed bubble plot is useful for determining significant component failures with respect to their failure consequences and occurrence likelihoods.