Prediction of Breaks in Municipal Drinking Water Linear Assets

Karimian, Farzad; Kaddoura, Khalid; Zayed, Tarek; Hawari, Alaa H.; Moselhi, Osama

doi:10.1061/(asce)ps.1949-1204.0000511

Cited by 10 publications

(4 citation statements)

References 57 publications

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“…For example, Marcelino et al (2021) used general machine learning to predict pavement performance. Karimian et al (2021) used an Evolutionary Polynomial Regression model to predict pipeline breaks. They clustered pipelines based on pipe age, diameter, length and material, and showed that pipelines of smaller diameter were more prone to failure.…”

Section: Predictive Analyticsmentioning

confidence: 99%

“…• Different methods and techniques have been proposed for locating and managing leaks in WMs (Misiunas 2005;Hamilton and Charalambous 2013;Fuchs-Hanusch 2019, 2020;Karimian et al 2021). What are the requirements of these approaches?…”

Section: Introductionmentioning

confidence: 99%

“…To what extent have the models met the expectation to predict the probability of future failures, time to next failure, and failure rate of pipe, or to predict whether or not a break will happen? • Failures reportedly could result from a large variety of factors: physical factors (e.g., pipe age, diameter, material, length, and wall thickness), environmental factors (e.g., soil type, climate, freeze/thaw properties, pipe bedding, trench backfill, traffic, and groundwater), and operational factors (e.g., number of pervious failures, water quality, internal water pressure, transient pressure, and leakage) (Stamou et al 2000;Wang et al 2009;Arsénio et al 2015;Lin and Yuan 2019;Karimian et al 2021). What are the main issues in data acquisition and quality?…”

Section: Introductionmentioning

confidence: 99%

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Asset management analytics for urban water mains: a literature review

Delnaz

2023

Environ Syst Res

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This study presents a review of the state-of-the-art literature on water pipe failure predictions, assessment of water losses risk, optimal pipe maintenance plans, and maintenance coordination strategies. In addition, it provides a categorization of water main (WM) failures as well as a taxonomy of WM maintenance strategies. In particular, predictive and prescriptive analytics are highlighted with the investigation of their contributions and drawbacks from methodological and application perspectives. This review aims at providing a review of failure analytics developed recently in water mains domain either for prediction of failure or identification of optimal maintenance strategies conjointly. Future research directions and challenges are elaborated in advancing the understanding about the mechanisms leading to failures. The existing gaps between theory and practice in managing assets across water distribution networks ensuring cost-effectiveness and reliability are discussed. As knowledge about the state of the water mains and related areas is crucial, thus, this review provides an state-of-the-art update from recent studies, and accordingly, presents and discusses avenues for future research.

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Section: Predictive Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asset management analytics for urban water mains: a literature review

Delnaz

2023

Environ Syst Res

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“…The time to failure of water mains was predicted using ANN considering pipe attributes, protection, and previous failure [105] as well as ridge regression (L2) and ensemble decision tree (EDT) [106]. In addition, the number of breaks of municipal drinking water mains was predicted by evolutional polynomial regression (EPR) [107]. Sensitivity analysis was performed in most studies to identify the most sensitive or important variables (e.g.…”

Section: Future Condition Prediction and Deterioration Modelingmentioning

confidence: 99%

Construction and maintenance of urban underground infrastructure with digital technologies

Wang

Yin

2022

Automation in Construction

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Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

Forero-Ortiz,

Martinez-Gomariz,

Sanchez-Juny

et al. 2023

Appl Water Sci

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There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.

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Prediction of Breaks in Municipal Drinking Water Linear Assets

Cited by 10 publications

References 57 publications

Asset management analytics for urban water mains: a literature review

Asset management analytics for urban water mains: a literature review

Construction and maintenance of urban underground infrastructure with digital technologies

Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

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