With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

A new seismic reliability evaluation model is proposed that quantifies the impact of earthquakes on hydraulic behavior of water supply networks. Probabilistic seismic events are produced in the target areas, and the depth of earthquake failure is evaluated by seismic reliability indicators. The developed model was applied to several case studies and used for an intensive examination on how a water supply system hydraulically responds to a seismic event and what system characteristics influence the system’s performance in the event of an earthquake. First, the system reliability of a real network in South Korea when subjected to earthquakes of various magnitudes and locations was quantified. Next, the reliabilities of full and simplified network models were evaluated to investigate how system layouts affect the reliability evaluation. Finally, networks with different configurations, pipe sizes, and system densities were compared with respect to the seismic reliability and various seismic damage ind...

A computer-based simulation model was developed to quantify the seismic damage that may occur in water supply networks and to suggest restoration strategies after such events. The model was designed to produce probabilistic seismic events and determine the structural damage of facilities. Then, the model numerically quantifies the system restoration rate over time by connecting it with a hydraulic analysis solver. The model intends to propose superb restoration plans by performing sensitivity analyses using several restoration scenarios. The developed model was applied to an actual metropolitan waterworks system currently operating in South Korea and successfully suggested the most efficient restoration approaches (given seismic damage) to minimize the complete recovery time and suspension of water service. It is expected that the proposed model can be utilized as a decision-making tool to determine prompt system recovery plans and restoration priorities in the case of an actual seismic hazard that may occur in water supply networks.

This paper describes a study conducted to develop a method to facilitate more reliable determination of the rehabilitation priority order for water pipes by taking into account the pipes’ hydraulic importance. Existing methods use only the pipeline deterioration rate to determine the rehabilitation priority order. Accordingly, the deterioration rate under normal conditions and the hydraulic importance under abnormal conditions of water distribution pipelines were classified according to two different attributes. The deterioration rate of a water distribution pipeline was calculated in terms of the deterioration rate due to pipeline information factors and the deterioration rate resulting from the installation environment/external factors. The hydraulic importance of water distribution pipelines was calculated by considering the importance of a single pipe failure caused by water leakage or an accident and that of a multiple pipe failure caused by a disaster, such as an earthquake. These four attribute factors were employed in a multi-criteria decision-making process called a weighted utopian approach, developed in this study, that determines the final rehabilitation priority order for each pipeline. The study results indicate that the rehabilitation priority order can be determined more easily using this approach than with previously-developed methods and that the model developed is easier and more convenient to apply than existing rehabilitation priority order models that require a large amount of data, as well as complex failure probabilities and mathematical models.