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 other way back) and the definition of two different time variables that can be analyzed in parallel. The first novelty makes the model less demanding in terms of data and software tools than others currently available, and the second one allows to get all the results after one single stage of calculation. To show its usability, the BPLA has been applied to a pipe network that supplies water to 500,000 citizens for which two years of failure records are available. Procedures and results have been compared to the well-known Weibull proportional hazard model (WPHM), with final relative errors lower than 10% and 15% on each particular result.
Highlights
If asset management is a key area in any urban water utility, an accurate knowledge of the pipes service life is a key aspect within asset management
Following a univariate parametric model, such analysis consists in finding the distribution that best fits the failure information contained in the basic pipes life assessment (BPLA) table
The Weibull distribution is the first recommended candidate because of its characteristics and flexibility, and the one considered in this paper, though there are other possible options
Summary
If asset management is a key area in any urban water utility, an accurate knowledge of the pipes service life is a key aspect within asset management. There are many advantages to knowing the current condition of pipes and their remaining life until renovation. Hydraulic operation and maintenance policies may be optimized; from an economic perspective, network expenditures and costs may be kept at minimum levels, and from a social perspective, the subsequent level of service may be maintained as high as possible. One main reference in determining the optimal replacement time of pipes dates back from 1979 by Shamir and Howard [1]. It consisted of a deterministic model for the economic optimization of repair vs renovation times. A good general approach to networks maintenance with an insight on failure forecasting was presented by
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