Model for Water Distribution System Reliability

Bao, Yixing; Mays, Larry W.

doi:10.1061/(asce)0733-9429(1990)116:9(1119)

Cited by 176 publications

(70 citation statements)

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“…According to the previous studies [6] [8], the optimal number of iterations is between 500 times to 3000 times. Because the results had tiny different after 2000 iterations, we use 2000 iterations in this study.…”

Section: Example Applicationmentioning

confidence: 99%

“…One is the Monte-Carlo simulation (MCS) technique, which can bring more accurate reliability estimation [5] [6], but requires a large number of trials to ensure accuracy thereby resulting in a computationally inefficient problem. Another is a linear probabilistic hydraulic model, called the first-order reliability method [7] [8], which can overcome the computationally inefficient problem in MCS, but has a lower accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

Peng¹,

Mayorga²

2016

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This paper develops an innovative approach to optimize a long-term rehabilitation and upgrading schedule (RUS) for a water distribution system with considering both hydraulic failure and mechanical performance failure circumstances. The proposed approach assesses hydraulic reliability dynamically and then optimizes the long-term RUS in sequence for a water distribution system. The uncertain hydraulic parameters are treated as random numbers in a stochastic hydraulic reliability assessment. The methodologies used for optimization in a stochastic environment are: Monte Carlo Simulation, EPANET Simulation, Genetic Algorithms, Shamir and Howard's Exponential Model, Threshold Break Rate Model and Two-Stage Optimization Model. The proposed approach is conducted on a simulation model of water distribution network in a computer by two universal codes, namely the hydraulic reliability code and the optimal RUS code. The applicability of this approach is verified in an example of a benchmark water distribution network.

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Section: Example Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

Peng¹,

Mayorga²

2016

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“…Measures of hydraulic reliability were investigated by Su et al (1987), Wagner et al (1988b), Bao & Mays (1990 and Cullinane et al (1992). Xu & Goulter (1998) calculated the probability of meeting nodal demands at or above a minimum prescribed pressure, while Ostfeld (2001) calculated the probability of annual zero shortage.…”

Section: T O W Nmentioning

confidence: 99%

“…probabilistic modelling. Monte Carlo simulation is commonly used in these simulation methods (Wagner et ai., 1988b;Bao & Mays, 1990;Yang et ai., 1996). Analytical methods have the advantage of requiring less computational effort compared to stochastic simulation methods.…”

Section: U N I V E R S I T Y O F C a P E T O W Nmentioning

confidence: 99%

Optimal Reliability-Based Design of Bulk Water Supply Systems

Chang

Zyl

2014

J. Water Resour. Plann. Manage.

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DJ :;sc rt(l 1I UIl s uhnlltt ed In p a rt i~l fulfi lm en t o~' th e reqlllrcrnen ts fu r tht., degree ofM~s t e r ofSClence In Enginee ring AbstractBulk water supply systems are usually designed according to deterministic design guidelines. In South Africa, design guidelines specify that a bulk storage reservoir should have a storage capacity of 48 hours of annual average daily demand (AADD), and the feeder pipe a capacity of 1.5 times AADD (CSIR, 2000). Nel & Haarhoff (1996) proposed a stochastic analysis method that allowed the reliability of a reservoir to be estimated based on a Monte Carlo analysis of consumer demand, fire water demand and pipe failures. Van Zyl et al. (2008) developed this method further and proposed a design criterion of one failure in ten years under seasonal peak conditions.In this study, a method for the optimal design of bulk water supply systems is proposed with the design variables being the configuration of the feeder pipe system, the feeder pipe diameters (i.e. capacity), and the size of the bulk storage reservoir. The stochastic analysis method is applied to determine a trade-off curve between system cost and reliability, from which the designer can select a suitable solution.Optimisation of the bulk system was performed using the multi-objective genetic algorithm, NSGA-II. As Monte Carlo sampling can be computationally expensive, especially when large numbers of simulations are required in an optimisation exercise, a compression heuristic was implemented and refined to reduce the computational effort required of the stochastic simulation. Use of the compression heuristic instead of full Monte Carlo simulation in the reliability analysis achieved computational time savings of around 75% for the optimisation of a typical system.Application of the optimisation model showed that it was able to successfully produce a set of Pareto-optimal solutions ranging from low reliability, low cost solutions to high reliability, high cost solutions. The proposed method was first applied to a typical system, resulting in an optimal reservoir size of approximately 22 h AADD and feeder pipe capacity of 2 times AADD. This solution achieved 9% savings in total system cost compared to the South African design guidelines. In addition, the optimal solution proved to have better reliability that one designed according to South African guidelines.A sensitivity analysis demonstrated the effects of changing various system and stochastic parameters from typical to low and high values. The sensitivity results revealed that the length of the feeder pipe system has the greatest impact on both the cost and reliability of the bulk system. It was also found that a single feeder pipe is optimal in most cases, and that parallel feeder pipes are only optimal for short feeder pipe lengths.The optimisation model is capable of narrowing down the search region to a handful of possible design solutions, and can thus be used by the engineer as a tool to assist with the design of the final system.

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“…Wagner et al [1988a] point out that both analytical and simulation methods are used to assess system reliability. Bao and Mays [1990] define a hydraulic reliability as the probability that the system can satisfy the flow demand at the required pressure head. They use Monte Carlo simulation of the hydraulic network equations to estimate the reliability.…”

Section: Motivation: Problem Definitionmentioning

confidence: 99%

Hydraulic power capacity of water distribution networks in uncertain conditions of deterioration

Park¹,

Lambert²,

Haimes³

1998

Water Resources Research

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Abstract. Water distribution systems throughout the United States are in various and uncertain stages of deterioration. Large capital expenditures are needed for their maintenance, modernization, and revitalization. Failures of the systems to supply adequate flow and pressure are adverse to the long-term economic growth and prosperity of the nation. Modeling the uncertain capacity of the distribution network is essential to allocating limited resources to components requiring the most rehabilitation. Network capacity and reliability are related to the uncertain conditions of pipes in the system. Some approaches to measuring a hydraulic capacity have been based on specifying flow volume alone without accounting for pressure. This paper introduces the combining of requirements for volume and pressure in a single dimension, the hydraulic power. Next, the hydraulic power capacity is defined as the probability that there exists a feasible flow of hydraulic power in the water distribution network. The probabilistic assessment of hydraulic power capacity is demonstrated in a small example under the assumptions of (1) fixed, known maximum inflows (supply) and minimum outflows (demand), (2) uncertain capacities for hydraulic power in individual pipes, reflecting uncertain conditions of pipe decay and roughness, and (3) an optional operating rule corresponding to a fixed minimum total loss of power in the network. Introduction Motivation: Problem DefinitionThe National Council on Public Works Improvement [1988] was created by Congress to assess the state of the U.S. infrastructure. According to the Council the condition of the nation's infrastructure is hardly encouraging. It identified a number of deficiencies in eight major areas; among these are water resources and water supply. The Council found that storage and distribution systems are deteriorating in older cities, with many of the pipes in these systems over 100 years old. The Comptroller General of the United States [1980] estimates that 20% of the pipes in Boston were installed before 1900, and in Washington, D.C., most of the current pipes were put into service in the late 1800s.Deteriorating systems pose a major concern for public works. One of the principal roles of public works is to provide water in sufficient quantity and quality to users. The utilities rely on vital physical assets including reservoirs, pipes, pumps, and valves that make up the public water distribution system. According to the Council the performance of these assets depends on a range of human and technical factors as well as on the ongoing level of capital investment and maintenance. It goes on to say that maintenance is perhaps the single most important element of governmental stewardship and obligation. Nevertheless, maintenance is often the first element to be deferred or altogether eliminated from the budget. Maintaining sufficiently reliable water distribution systems is one of the elements of a sound infrastructure which depends on the longterm economic growth and prosperity of ...

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Model for Water Distribution System Reliability

Cited by 176 publications

References 10 publications

Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

Hydraulic Reliability Assessment and Optimal Rehabilitation/Upgrading Schedule for Water Distribution Systems

Optimal Reliability-Based Design of Bulk Water Supply Systems

Hydraulic power capacity of water distribution networks in uncertain conditions of deterioration

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