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Scheduling rehabilitation of pipes is essential for efficient maintenance and management of Water Distribution Systems. In reality, however, rehabilitation plans are more focused on subareas than on each pipe due to unpredictable variables: cut off time of water supply, number of traffic control times, and location of valves for example. This study determines rehabilitation priority order of subareas considering relative importance as well as deterioration of pipe. Water network is divided into several subareas according to valve locations and segments. Relative importance of pipes is included in the calculation process of rehabilitation priority order to consider hydraulic condition of the network. Then the relative importance of pipes is calculated on two failure simulations. The first simulation takes segments and unintended isolations to evaluate the relative importance according to the number of customers who are forced out of service when the segment is shut down. The second simulation develops Effect Index by considering abnormal condition which is produced when fluctuation of water causes sudden change of pressure. The decision making process adapts Weighted Utopian Approach by adding five weighting methods to get a result. The developed method provides practical results in the application to real networks. In this point, the model clearly has advantages over existing ones and can replace those in practice.

This study adopts the Harmony Search (HS) meta-heuristic algorithm (Geem et al., 2001; Kim et al., 2001) to calibrate a hydraulic simulation model (EPANET) for C-Town. The algorithm conceptualizes a musical process of searching for a perfect state of harmony (optimal solution) and allows a random search without initial values, thus removing the necessity for information on derivatives. The calibration procedure is implemented in a manner that the sum of errors between two data sets: the measured SCADA data and the values derived from the HS algorithm. The calibration process is implemented into three stages – preprocess, rough tuning, and fine tuning. In the preprocess stage the original SCADA data and pump control rules are modified to complement missing data and operation rules. In the rough and fine tuning stages the C-town network is divided into five sub-networks based on supply zones. Pipe roughness factors in each sub-network are calibrated by pipe diameters which are divided into five groups. Therefore, total number of decision variables in each sub-network is 168 nodal water demand factors and 5 pipe roughness coefficients. The entire C-town network is also calibrated using each already calibrated sub-network as the initial populations. It is found that the simple network in the downstream end can be calibrated separately; thus, the network division does accelerate the convergence speed. Several parameters of the HS algorithm such as HMCR and PAR can be adjusted to find better calibration results

The optimal design of water distribution system has been usually performed with fixed hydraulic variables and single objective. However, a realistic water distribution system should take inherent uncertainties of data into consideration. This study suggests a method to minimize the system cost and maximize the robustness of network based on uncertainties in nodal demands and pipe roughness coefficients. Multi-Objective Genetic Algorithm (MOGA) implements two separate optimization models for the least cost and the best robustness design as the initial population. The model considers the uncertainties in roughness coefficient and water demand by using Latin Hypercube sampling technique with the assumption of beta probability density function. Several beta probability density functions with wide range of data are evaluated in the procedure. The proposed approach is tested in case study of the

It is of utmost importance for public health to identify optimal location for sensors to detect all possible contamination events. However, it has been difficult due to several reasons including the complex nature of water distribution system, inadequate technologies to detect every contaminant, and most of all, limited budget. The usual techniques for optimizing sensor locations require significant computational power and time, thus making it difficult to apply in real systems. This study simplifies the computation by utilizing network analysis, betweenness centrality, and shortest path algorithm. Betweenness centrality defines the centrality of a node in terms of degree to which the node falls on the shortest path between other pairs of nodes. In addition to network analysis, Multi-Objective Genetic Algorithm and travel time matrix are used to consider the sensitivity of flow directions in pipes, presenting Pareto solutions. Consequently, the proposed method will minimize detection time and number of sensors needed. It is expected to benefit the most when the water distribution network is large and complicated.