Journal of Water Resources Planning and Management

Because of the large computational burden associated with the direct assessment of reliability, the indirect indices of reliability have recently received more attention in the framework of water distribution system design. Two new energy-related indices for reliability evaluation (i.e., available power index and pipe hydraulic resilience index) are developed in this paper. The performance of these new indices is evaluated and compared with that of four existing indices (three other energy-related indices—i.e., resilience index, network resilience index, and modified resilience index—and the entropy-based method, i.e., diameter-sensitive flow entropy) according to the following two-step methodology. In the first step, the application of the multiobjective optimization makes it possible to determine optimal network configurations that trade-off the installation cost (to be minimized) against the generic indirect reliability index (to be maximized). In the second step, the performance of the optimal solutions in terms of explicit reliability assessment is examined under conditions in which the original network is perturbed by applying demand variations and random pipe failures to account for future operating uncertainties. The Hanoi and the Fossolo benchmark networks are used as case studies. The results obtained show that energy-based indices yield an overall superior estimate of reliability in comparison with the diameter-sensitive flow entropy. Furthermore, the new indices show some advantages in the evaluations performed under demand and pipe failure uncertainties.

Prompt detection of intentional or accidental contamination of the public water supply is vital to maintain public health in any centralized water distribution system. Being able to quickly detect a system contamination event may be the single most influential factor to reduce possible contamination fallout. Consequently, major research has explored how to best protect a water distribution system (WDS) through strategic placement of fixed water quality monitoring stations. Although fixed monitoring stations within a wireless sensor network (WSN) are robust with respect to hydraulic conditions, the stations are expensive to place, and may not provide the highest spatial and temporal resolution of contamination detection. This work sets to build the understanding of a mobile wireless sensor network (MWSN) where inline mobile sensors function within water in pipes to monitor water quality and to wirelessly transmit data to fixed transceivers in real time. Mobile sensor behavior was modeled alongside contamination simulations and the deployment of fixed and mobile sensors was together optimized to minimize the affected population prior contamination event detection constrained by a total system cost. Results show a MWSN to be highly sensitive to sensor battery life, transceiver network coverage, and total system cost. Future obstacles for implementation of a MWSN are highlighted and discussed to be address in future work.

The first-order sensitivity matrices (matrices of sensitivity or influence coefficients) have application in many areas of water distribution system analysis. Finite-difference approximations, automatic differentiation, sensitivity equations, and the adjoint method have been used in the past to estimate sensitivity. In this paper new, explicit formulas for the first-order sensitivities of water distribution system (WDS) steady-state heads and flows to changes in demands, resistance factors, roughnesses, relative roughnesses, and diameters are presented. The formulas cover both pressure-dependent modeling (PDM) and demand-dependent modeling (DDM) problems in which either the Hazen-Williams or the Darcy-Weisbach head-loss models are used. Two important applications of sensitivity matrices, namely calibration and sensor placement, are discussed and illustrative examples of the use of sensitivity matrices in those applications are given. The use of sensitivity matrices in first-order confidence estimation is briefly discussed. The superior stability of the PDM formulation over DDM is established by the examination of the sensitivity matrices for the same network solved by both model paradigms. The sensitivity matrices and the key matrices in both the global gradient method for DDM problems and its counterpart for PDM problems have many elements in common. This means that the sensitivity matrices can be computed at marginal cost during the solution process with either of these methods.

In this paper, the nonhomogeneous Poisson process (NHPP), hierarchical beta process (HBP), and a new-developed Bayesian simple model (BSM) are used for short-term failure prediction with several water utility failure data sets. The prediction curve was used to investigate the relative ranking of the pipes from the three models. The curve was demonstrated to be more suitable for practical situations compared to the traditional receiver operating characteristic (ROC) curve. The expected number of failures and the effect of incorporating covariates by dividing the pipe assets into smaller pressure cohorts were also examined. Based on the data sets used, the observations from the prediction curves show that the performance of the three models are very similar in terms of pipe ranking. However, the BSM is more advantageous because of its relative simplicity. The covariate, the number of known past failures, plays a very important role when considering the relative ranking of the pipes in the network. The NHPP and HBP are recommended if the total number of failures in the network is required. Subdividing one of the data sets (Data Set A) into a smaller pressure cohort showed improvement only for the prediction by the BSM. Unlike the NHPP, time is not specifically included in the HBP and BSM. Therefore, the HBP and BSM are not suitable for long-term predictions and cannot include time-dependent covariates.

This study proposes a seismic reliability–based water distribution system (WDS) optimal design model that minimizes total cost and maximizes seismic reliability. Here, seismic reliability is defined as the ratio of the available quantity of water to the required demand under stochastic earthquake events. A new evaluation model is used to assess seismic reliability, while a multiobjective harmony search (MOHS) based on a ranking approach is used for optimization. The Anytown network was modified for the demonstration of the proposed method. First, this study performs the sensitivity analysis of MOHS parameter values [i.e., harmony search consideration rate (HMCR) and pitch adjustment rate (PAR)] to identify the best parameter set in a pipe-sizing problem of an Anytown network. Then, Pareto optimal solutions with three different tank configurations are obtained and compared with respect to the final Pareto fronts and the system designs. For the sensitivity analysis, it reveals that higher PAR and lower HMCR values are also required to maintain high searchability in a multiobjective (MO) framework. In addition, Pareto-optimal solutions found for networks with tanks dominated those found for those without tanks.