Exploration of the relationship between water main breaks and temperature covariates

Rajani, Balvant; Kleiner, Yehuda; Sink, Jean-Eric

doi:10.1080/1573062x.2011.630093

Cited by 47 publications

(40 citation statements)

References 6 publications

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“…The DI pipe average age of 16 years was recorded versus 22 years for CI pipe. Folkman [33] and Rajani et al [34] reported similar findings in other networks in the Greater Toronto. This finding is due to the nature of the soil around the pipes that triggers the pipe corrosion that mainly affects DI pipes leading to pitting and hole failures.…”

Section: Pipe Failures In Greater Toronto Areasupporting

confidence: 70%

“…This finding is due to the nature of the soil around the pipes that triggers the pipe corrosion that mainly affects DI pipes leading to pitting and hole failures. This average age at the first recorded failure implies that the soil has moderate corrosion [34]. Figure 6 also shows that the average time between subsequent failures for CI is 2.5 years, which is relatively larger than that of 1 year for DI.…”

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 86%

“…It is observed that the circumferential failure is the main failure type for CI pipes, while the hole failure is the main in DI pipes constituting more than 90% of the failure types in the network. The CI pipes tend to show higher failure rates in winter months, January and February [29,34]. This is due to the external applied circumferential pressure exerted on the pipe circumference under the effect of frozen ground, where pipes tend to break more easily.…”

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 94%

“…Unlike the nonhomogeneous CI pipes, the DI pipes can resist externally applied pressure and thus experience fewer circumferential failures. DI pipes tend to break in localized areas caused by corrosion pitting and weakening the pipe material [34].…”

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 99%

“…In all pipe types, the effects of CP and CML protection is additive leading to an increase in the time to next failure. This specific behavior is related to the studied network since various impacts of CP and CML protection have been reported for other networks under different conditions of soil corrosiveness, temperature, and installation methods that have impacts on the coatings [34].…”

Section: Parametric Analysis Of Developed Elm Modelmentioning

confidence: 99%

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Extreme learning machine model for water network management

Sattar

Ertuğrul

Gharabaghi

et al. 2017

Neural Comput & Applic

106

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A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/ rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.

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Section: Pipe Failures In Greater Toronto Areasupporting

confidence: 70%

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 86%

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 94%

Section: Pipe Failures In Greater Toronto Areamentioning

confidence: 99%

Section: Parametric Analysis Of Developed Elm Modelmentioning

confidence: 99%

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Extreme learning machine model for water network management

Sattar

Ertuğrul

Gharabaghi

et al. 2017

Neural Comput & Applic

106

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Pipeline Failure and Deterioration Models

Kabir¹,

Tesfamariam²,

Sadiq³

2019

Encyclopedia of Water

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The utility managers and other authorities rely on location‐specific pipeline failure information to develop and implement long‐ and short‐term management plans. The pipeline failure or deterioration depends on multiple pipe dependent, time‐dependent, and pipe and time‐dependent factors. For this, substantial efforts have been made to develop pipeline failure or deterioration prediction models using different statistical models like regression‐based, survival analysis‐based, and Bayesian inference‐based models. The performance of these models depends significantly on the quantity and quality of available data. Most prediction models focus on larger utilities where sufficient amount of pipeline failure and other related information is available. Small and medium‐size utilities often suffer due to technical and financial resource shortages and data scarcity. It is essential to consider model uncertainties for pipeline failure or deterioration prediction model development. In this study, a brief review of existing regression‐based, survival analysis‐based, and Bayesian inference‐based pipeline failure prediction models are presented. To demonstrate the applicability of different pipeline failure and deterioration prediction models, the pipeline failure data of the City of Calgary is considered.

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Data and Analytics Combat Water Main Failures

Grigg¹

2019

Journal AWWA

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Exploration of the relationship between water main breaks and temperature covariates

Cited by 47 publications

References 6 publications

Extreme learning machine model for water network management

Extreme learning machine model for water network management

Pipeline Failure and Deterioration Models

Data and Analytics Combat Water Main Failures

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