Water Distribution Systems are one of the most substantial and vulnerable part of civil infrastructure systems. For the reason that many large water distribution systems are old, which results in more leakage and expenses (e.g., increasing pump head, pipe burst, constituents’ replacement), a significant portion of water produced by the utilities never passes through the consumers’ meters. Due to the complex nature and vast spatial extent of a water distribution system it may be difficult for the utility personnel to identify and fix the leaks, therefore it is imperative to develop software frameworks for modeling and analyzing leakage in water distribution system during ordinary operational conditions as well as unexpected events. In this paper a Bayesian approach with Markov chain Monte Carlo method is implemented to map probabilistic characterizations of water leakage. If for this purpose physical parameters such as pipe vintage, material, and loading are available, they can be are used to develop prior information; otherwise, a uniform prior may be assumed. Routinely measured water quality, pressure, and flow measurements together with the uncertainty in demand are used to develop the likelihood function. The analyses are facilitated through the EPANET water distribution simulation tool. The efficiency and versatility of the proposed methodology is examined using water distribution network.

A new method is proposed for the least cost design of looped piped networks under various levels of redundancy using a genetic algorithm. Various levels of redundancy can be obtained by considering the number of pipes to be taken out of service at any one time. As a result, a tradeoff curve of redundancy and cost can be developed. The approach is being extended to consider different measures of redundancy as well as other performance criteria. Such extensions are more computationally demanding and are therefore being implemented using a distributed GA.