Risk-based prioritization of water main failure using fuzzy synthetic evaluation technique

Al-Zahrani, Muhammad A.; Abo-Monasar, Amin; Sadiq, Rehan

doi:10.2166/aqua.2015.051

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Al‐Zahrani et al. (2016) developed a fuzzy‐based model that uses 13 input factors relating to the structural integrity of the pipes, water quality, and operation of the network. These factors were arranged in a hierarchy, and fuzzy membership functions were defined for each of the factors based on the characteristics of each factor obtained in the literature (AWWA, 2002; Sarbatly & Krishnaiah, 2007).…”

Section: Machine Learning‐based Modelsmentioning

confidence: 99%

“…The risk index is a multiplication of failure probability and consequences of water pipes. Similar to the approach employed by Al-Zahrani et al (2016), the failure probability index for each water pipe was determined by aggregating the fuzzy sets with the weights of each factor obtained from the AHP analysis. Afterward, the risk indices were developed, and the most probable pipes to fail were visualized using the GIS map.…”

Section: Fuzzy-based Modelsmentioning

confidence: 99%

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Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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Water distribution networks (WDNs) are undoubtedly a critical part of water utility assets, as they are responsible for water transmission (Atef et al., 2016;Berardi et al., 2014). It has been reported that, on average, 80% of water utility expenditures are spent on the management of WDNs (Poulakis et al., 2003). However, available evidence shows that the failure of WDNs is increasing at a fast rate, which negatively impacts individuals' social, economic, and health status (Steffelbauer et al., 2022;Yazdani & Jeffrey, 2012).In the USA and Canada, around 700 water pipes fail daily, thereby contributing to the loss of over 2 trillion gallons of clean water annually (Fan et al., 2022). In 2017, more than 2.2 billion m 3 of water was loss in China (Liao et al., 2021). The enormous financial commitment associated with the failure of water distribution networks cannot be overemphasized. According to the American Water Work Association (AWWA), the USA needs to invest around $1 trillion in replacing and repairing the deteriorating components of their WDNs (Fan et al., 2022). In Australia, the estimated cost of repairing and maintaining WDNs is about AUD 1.4 billion (Weeraddana et al., 2020). In South Korea, 52.5% of the water pipes will require rehabilitation by 2024, as indicated in a study by Seo et al. (2015). In the case of Colombia, a developing country, approximately 50% of water is lost due to water pipe failures (Giraldo-González & Rodríguez, 2020). From the aforementioned, the failure of WDNs, which mostly consist of water pipes, is a global issue that requires utmost attention. WDNs are founded underground; therefore, these infrastructures need to be designed to resist traffic, soil, internal, and overburden pressures, and other environment and operation related loads (Berardi et al., 2008). In essence, the reliability of WDNs must not be compromised during their service life; hence, catastrophic failure

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Section: Machine Learning‐based Modelsmentioning

confidence: 99%

Section: Fuzzy-based Modelsmentioning

confidence: 99%

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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“…Since, the network renewal involves the replacement of pipes and creates more cost than other methods, the current failure rates, operation, the costs of maintenance and repair, new resource, energy and initial investment and operating should be considered and analyzed (Al-Zahrani, Abo-Monasar, & Sadiq, 2016;Venkatesh, 2012;Mondaca, Andrade, Choi, & Lansey, 2015;Francisque et al, 2017;Cavaliere, Maggi, & Stroffolini, 2017). The useful life focuses on situations where leaks cannot be managed economically through failure repair, PM and other methods.…”

Section: Cost Analysis Standard For Network Renewalmentioning

confidence: 99%

Defining cost standard and new algorithm for economic leakage level components in water loss management

et al. 2022

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Methods and tools used to reduce leakage in distribution systems are often time consuming and costly and require special requirements. Therefore, cost-benefit analysis is very important for basic reduction methods applied in water loss management. In this study, cost and benefit analysis standards were developed for basic methods such as pressure management, number of teams, and pipe rehabilitation and active leakage control, in managing leakages. Moreover, a new cost algorithmic structure was developed and the economically recoverable water amount was determined by applying calculation tool developed to make detailed analyzes systematically and accurately. The most important advantage of this study is the development of an economic analysis model and algorithmic structure for basic reduction methods according to field data. It is thought that the cost analysis and algorithmic structures developed will make a significant contribution to the economic leakage level analysis and serve as a reference for sustainable water loss management.

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“…Fuzzy logic uses fuzzy sets and rule matrices to classify or categorise samples. This technique has been implemented in many water supplies studies to group pipes or regions of the networks according to their risk of failure (Al-Zahrani, Abo-Monasar & Sadiq, 2016;Islam, Sadiq, Rodriguez, Najjaran, Francisque & Hoorfar, 2013;Salehi, Jalili Ghazizadeh & Tabesh, 2018). Nevertheless, in all these studies the rules are generated based on expert opinions.…”

Section: Models: Characteristics and Applicationsmentioning

confidence: 99%

Trends and applications of machine learning in water supply networks management

Robles-Velasco

Muñuzuri

Onieva

et al. 2021

JIEM

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Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted.Design/methodology/approach: For this purpose, eight studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance.Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in all revised studies to train and validate their models.Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the models.

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Risk-based prioritization of water main failure using fuzzy synthetic evaluation technique

Cited by 8 publications

References 24 publications

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Defining cost standard and new algorithm for economic leakage level components in water loss management

Trends and applications of machine learning in water supply networks management

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