By the same authors

Exploring techniques to improve large-scale drainage system maintenance scheduling using a risk driven model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published copy (DOI)

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Publication details

Title of host publicationOperations Research and Enterprise Systems - 5th International Conference, ICORES 2016, Revised Selected Papers
DatePublished - 15 Feb 2017
Pages161-179
Number of pages19
PublisherSpringer-Verlag
EditorsBegona Vitoriano, Greg H. Parlier
Volume695
Original languageEnglish
ISBN (Print)9783319539812

Publication series

NameCommunications in Computer and Information Science
Volume695
ISSN (Print)18650929

Abstract

Gully pots are part of the infrastructure of a storm drain system, designed to drain surface water from streets. Any broken or blocked gully pot represents a potential cause of flooding, for instance during periods of intense or prolonged rainfall. Regular cleaning is necessary for gully pots to function effectively. We model gully pot maintenance as a risk-driven problem, and evaluate the maintenance quality of maintenance strategies by considering the risk impact of gully pot failure and failure behaviour. The results suggest investment directions and management policies that potentially improve the efficiency of maintenance. We find that the current maintenance quality is significantly affected by untimely system status information. We propose that low-cost sensor techniques might be able to improve timeliness of status information, and use simulation results to show the behaviour and advantages that might arise in a range of real-world scenarios.

Bibliographical note

Funding Information:
The authors would like to thank Gaist Solutions Ltd. for providing data and domain knowledge. This research is part of the LSCITS project funded by the Engineering and physical sciences research council (EPSRC).

Publisher Copyright:
© Springer International Publishing AG 2017.

    Research areas

  • Gully-pot system maintenance, Risk management, Simulation

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