Investigation of a static and a dynamic neighbourhood methodology to develop work programs for multiple close municipal infrastructure networks

Abstract

Interventions on infrastructure networks in cities cause disruptions to the services provided by those but also to other networks that have to be at least partially shut down for the interventions executed. Due to these effects, there is substantial benefit to be obtained by grouping interventions on networks that are spatially close to one another. This benefit is principally due to reduced costs of intervention and reduced service disruption. In this paper, two intervention grouping methodologies to develop work programs for infrastructure networks are investigated. The first is based on static, the second is based on dynamic grouping. The two methodologies are investigated by developing work programs on multiple infrastructure networks in an urban area and compared against the same methodologies, albeit without coordination. In the example, interventions on the objects of five different infrastructure networks are grouped based on failure probability of the objects and their closeness. It is found that the dynamic grouping methodology results in work programs that result in a better consideration and prioritisation of objects that are in urgent need for an interventi, while accounting for the synergies that can be created due to efficient coordination. The advantages, disadvantages and future research directions are discussed.

Keywords:

• Infrastructure
• maintenance
• civil engineering
• sewers & drains
• pipes & pipelines

Notes

No potential conflict of interest was reported by the authors.

1 Theoretically, all regular area tessellations (triangles, squares or hexagons) are possible.

2 A powerful magnet is used to magnetise the steel walls. At areas where there is corrosion or deformed metal, the magnetic field ‘leaks’ from the steel. By interpreting the magnetic leakage field, it is possible to identify damaged areas and estimate the depth of metal loss.

3 i.e. models that allow for better condition development predictions.

4 For objects that are only described by two states (in operation/defunct), this is directly the failure probability. For objects with multiple condition states, this is the probability of not providing the requested service, which can be the case in more than one condition state.

5 .

6 The sign function is defined as follows: .

7 e.g. .

8 The spatial distribution has been slightly altered due to security concerns of the participating city.

9 In this case study, risk was proxied by the product of age (proxying failure probability) and importance factor ( proxying consequences). A detailed discussion can be found in Sections 5.2 and 5.3.

10 Simplified: A Voronoi cell defines the region around an object, where no other object is closer.