2020
DOI: 10.3390/w12030667
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Abstract: In any water utility, a reliable assessment of the service life of the network pipes is a key piece within the big puzzle of assets management. This paper presents a new statistical model (basic pipes life assessment, BPLA) to assess the service life of pipes, to locate the pipes on the failures bath curve and to forecast the expected failures in future years. Its main novelties are the processing of pipe information (is that information what is adapted to the classical maintenance engineering and not the othe… Show more

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Cited by 3 publications
(1 citation statement)
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“…Predicting pipe failures requires several variables correlated with the various modes and mechanisms of failure (for further detail see Barton et al (2019Barton et al ( , 2020). Collecting variables is a complex task, and pipe failure models are ordinarily developed based on limited data because variables are not routinely collected, are unnecessary for regulatory reporting requirements or immediate business needs (see section 2.1), or are considered too costly to acquire due to budget restrictions (Ramirez et al 2020). As one participant noted 'In a situation where water companies are squeezed for budget, things that look like a 'nice to have' get squeezed out.'…”
Section: An Incomplete Range Of Correlated Variablesmentioning
confidence: 99%
“…Predicting pipe failures requires several variables correlated with the various modes and mechanisms of failure (for further detail see Barton et al (2019Barton et al ( , 2020). Collecting variables is a complex task, and pipe failure models are ordinarily developed based on limited data because variables are not routinely collected, are unnecessary for regulatory reporting requirements or immediate business needs (see section 2.1), or are considered too costly to acquire due to budget restrictions (Ramirez et al 2020). As one participant noted 'In a situation where water companies are squeezed for budget, things that look like a 'nice to have' get squeezed out.'…”
Section: An Incomplete Range Of Correlated Variablesmentioning
confidence: 99%