Identifying critical elements in drinking water distribution networks using graph theory

Journal Article (2021)
Author(s)

Didrik H. Meijer (Deltares, TU Delft - Sanitary Engineering)

Johan Post (Partners4UrbanWater)

Jan Peter van der Hoek (TU Delft - Sanitary Engineering, Waternet)

Hans Korving (Deltares)

Jeroen G. Langeveld (Partners4UrbanWater, TU Delft - Sanitary Engineering)

F. Clemens (Deltares, TU Delft - Sanitary Engineering)

Research Group
Sanitary Engineering
Copyright
© 2021 D.H. Meijer, Johan Post, J.P. van der Hoek, Hans Korving, J.G. Langeveld, F.H.L.R. Clemens
DOI related publication
https://doi.org/10.1080/15732479.2020.1751664
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 D.H. Meijer, Johan Post, J.P. van der Hoek, Hans Korving, J.G. Langeveld, F.H.L.R. Clemens
Research Group
Sanitary Engineering
Issue number
3
Volume number
17
Pages (from-to)
347-360
Reuse Rights

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Abstract

Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on the most critical elements of a WDN, with respect to the functioning of the system as a whole, is essential for prioritising maintenance or rehabilitation activities. In this study a Graph theory based method is developed and applied for efficiently identifying the most critical elements. The main advantage of this method is that it avoids the need to perform elaborate hydrodynamic model calculations. Instead, the structure of the network is the main starting point. The results show that the structure of the network is more decisive than the hydraulics with respect to the criticality of the system’s performance as a whole. Results depict that the suggested approach is applicable not only to the main (primary) network, but also to the capillaries which are normally beyond the scope of the traditional methods applied so-far because of the complexity of the networks and the required calculation time.