Reliability Tester for Water-Distribution Networks

Khomsi, Driss; Walters, Godfrey A.; Thorley, A.; Ouazar, Driss

doi:10.1061/(asce)0887-3801(1996)10:1(10)

Cited by 57 publications

(54 citation statements)

References 19 publications

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“…The approach (with 3 neurons at the output) adopted here differs radically from the ways of predicting the failure intensity (failure rate) indicator proposed in the earlier paper by the author [5] where separate ANN models would be created to predict the indicators for distribution pipes and house connections. Relatively large discrepancies between the experimental results and the AI values calculated from relation (3) proposed by other authors [20,21] are noticeable. Also Tabesh et al [21] compared the results of modelling by means of ANN with the ones calculated from the relation proposed by Khomsi et al [20].…”

Section: Resultsmentioning

confidence: 52%

“…The experimental availability indicator was then compared with the one predicted by means of artificial neural networks. In addition, it was checked whether the values calculated from the formula proposed by Khomsi et al [20] were similar to the ones obtained from the modelling by means of ANNs.…”

Section: Reliability and Failure Analysismentioning

confidence: 99%

“…According to the authors [20], the model developed by them can be used not only to determine the reliability level of the existing water supply networks, but also to design new water distribution networks. Authors [20,21] proposed following relationship for availability indicator calculation:…”

Section: Reliability and Failure Analysismentioning

confidence: 99%

“…They indicated the advantages and disadvantages of the models used to determine the failure frequency of water supply networks. A few years earlier Khomsi et al [20] had used computer simulation (a program written in TURBO PASCAL) to estimate the operational reliability level of a water distribution network. According to the authors [20], the model developed by them can be used not only to determine the reliability level of the existing water supply networks, but also to design new water distribution networks.…”

Section: Reliability and Failure Analysismentioning

confidence: 99%

“…A few years earlier Khomsi et al [20] had used computer simulation (a program written in TURBO PASCAL) to estimate the operational reliability level of a water distribution network. According to the authors [20], the model developed by them can be used not only to determine the reliability level of the existing water supply networks, but also to design new water distribution networks. Authors [20,21] proposed following relationship for availability indicator calculation:…”

Section: Reliability and Failure Analysismentioning

confidence: 99%

See 4 more Smart Citations

Neural Network Approach for Availability Indicator Prediction

Kutyłowska

2017

Period. Polytech. Civil Eng.

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The principal aim of this research was to find out if artificial neu- IntroductionResearch on the frequency of failure of water supply networks has been conducted in Poland and in the world for many years [1][2][3][4]. Still, besides determining the failure frequency level of water conduits on the basis of operating data, it is also necessary to supplement such studies with the results of the modelling, using typical models or artificial intelligence and Monte Carlo simulations: probability of the failure occurrence and values of failure rate [5][6][7][8] or the amount of water losses through pipe breaks [9]. Therefore one of the aims of this research was to show that mathematical modelling could be used to determine the availability indicator and reliability level of water conduits.

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Section: Resultsmentioning

confidence: 52%

Section: Reliability and Failure Analysismentioning

confidence: 99%

Section: Reliability and Failure Analysismentioning

confidence: 99%

Section: Reliability and Failure Analysismentioning

confidence: 99%

Section: Reliability and Failure Analysismentioning

confidence: 99%

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Neural Network Approach for Availability Indicator Prediction

Kutyłowska

2017

Period. Polytech. Civil Eng.

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A diameter‐sensitive flow entropy method for reliability consideration in water distribution system design

Liu

Savić

Kapelan

et al. 2014

Water Resources Research

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Flow entropy is a measure of uniformity of pipe flows in water distribution systems. By maximizing flow entropy one can identify reliable layouts or connectivity in networks. In order to overcome the disadvantage of the common definition of flow entropy that does not consider the impact of pipe diameter on reliability, an extended definition of flow entropy, termed as diameter-sensitive flow entropy, is proposed. This new methodology is then assessed by using other reliability methods, including Monte Carlo Simulation, a pipe failure probability model, and a surrogate measure (resilience index) integrated with water demand and pipe failure uncertainty. The reliability assessment is based on a sample of WDS designs derived from an optimization process for each of the two benchmark networks. Correlation analysis is used to evaluate quantitatively the relationship between entropy and reliability. To ensure reliability, a comparative analysis between the flow entropy and the new method is conducted. The results demonstrate that the diameter-sensitive flow entropy shows consistently much stronger correlation with the three reliability measures than simple flow entropy. Therefore, the new flow entropy method can be taken as a better surrogate measure for reliability and could be potentially integrated into the optimal design problem of WDSs. Sensitivity analysis results show that the velocity parameters used in the new flow entropy has no significant impact on the relationship between diameter-sensitive flow entropy and reliability.

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Sobol Sequence-based MOSaDE Algorithm for Multi-objective Design of Water Distribution Networks

Sirsant

Reddy

2022

Studies in Computational Intelligence

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Reliability Tester for Water-Distribution Networks

Cited by 57 publications

References 19 publications

Neural Network Approach for Availability Indicator Prediction

Neural Network Approach for Availability Indicator Prediction

A diameter‐sensitive flow entropy method for reliability consideration in water distribution system design

Sobol Sequence-based MOSaDE Algorithm for Multi-objective Design of Water Distribution Networks

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