An evolutionary fuzzy system to support the replacement policy in water supply networks: The ranking of pipes according to their failure risk

Robles-Velasco, Alicia; Muñuzuri, Jesús; Onieva, Luis; Cortés, Pablo

doi:10.1016/j.asoc.2021.107731

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Failures in water distribution networks can bring inconvenience and losses to both the population and economic activities, industry, and agriculture [2,3]. In this context, predicting future failures or breaks in a water supply network allows the management of this network to carry out planned interventions, anticipating failures, which can reduce inconvenience and losses caused by water supply interruption [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Medeiros,

dos Santos,

Brito

2024

Preprint

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This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

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Section: Introductionmentioning

confidence: 99%

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Medeiros,

dos Santos,

Brito

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Failures in these networks can bring inconvenience and losses to the population and economic activities, industry, and agriculture [2,3]. In this context, predicting future failures or breaks in a water supply network allows the management of this network to carry out planned interventions, anticipating failures, which can reduce inconvenience and losses caused by water supply interruption [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Medeiros,

dos Santos,

Brito

2024

Water

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This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities.

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“…In terms of pipe network optimization, Safavi and Geranmehr [9] proposed a mathematical model for sewage pipe network optimization which included the material cost and construction cost under a given pipe network layout, Haghighi and Bakhshipour [10] proposed a sewage pipe network optimization model that applied adaptive genetic algorithm to deal with nonlinear and discrete problems, Tian et al [11] applied discrete enumeration method to research the design and optimization of urban sewage transporting systems, Wang et al [12] did the optimized calculation of water supply network through improved ant colony algorithm. Wols et al [13] studied effects of climate conditions on the failures of drinking water distribution pipelines in Netherlands, Robles-Velasco et al [14] studied an evolutionary fuzzy system to predict unexpected pipe failures in water supply networks, Robles-Velasco et al [15] studied an artificial neural networks to forecast failures in water supply pipelines.…”

Section: Introductionmentioning

confidence: 99%

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

Luo³

et al. 2021

Period. Polytech. Civil Eng.

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With considering sewage pipe network upgrading projects in the “villages” in cities, the optimization of construction resources and the assessment of delay risks could be achieved. Based on the schedule-cost hypothetical theory, the mathematical model with constraint indicators was established to obtain the expression of optimal resource input, and conclude the method to analyze the schedule uncertainties. The analysis showed that cyclical footage of pipe could be regarded as a relatively fixed value, and the cost can be regarded as a function that depending on the number of working teams. The optimal number of teams and the optimal schedule occurred when the minimum total cost achieved. In the case of insufficient meteorological data, the Monte Carlo simulation method and uncertainty analysis method can be applied to assess the impact of rainfall on the total construction period, correspondingly the probability of such risk could be derived. The calculation showed that the risk of overdue completion varied significantly according to the construction starting time. It was necessary to take rainfall risk into consideration and make corresponding strategies and measures.

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An evolutionary fuzzy system to support the replacement policy in water supply networks: The ranking of pipes according to their failure risk

Cited by 10 publications

References 38 publications

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

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