Gatsoulis, I, Mehmood, MO, Dimitrova, VG et al. (5 more authors) (2016) Learning the Repair Urgency for a Decision Support System for Tunnel Maintenance. In: Kaminka, GA, Fox, M, Bouquet, P, Hüllermeier, E, Dignum, V, Dignum, F and Van Harmelen, F, (eds.) Proceedings. ECAI 2016: 22nd European Conference on Artificial Intelligence, 29 Aug - 02 Sep 2016, The Hague, Netherlands. Frontiers in Artificial Intelligence and Applications (285). IOS Press , Amsterdam, Netherlands , pp. 1769-1774. ISBN 978-1-61499-671-2
Abstract
The transport network in many countries relies on extended portions which run underground in tunnels. As tunnels age, repairs are required to prevent dangerous collapses. However repairs are expensive and will affect the operational efficiency of the tunnel. We present a decision support system (DSS) based on supervised machine learning methods that learns to predict the risk factor and the resulting repair urgency in the tunnel maintenance planning of a European national rail operator. The data on which the prototype has been built consists of 47 tunnels of varying lengths. For each tunnel, periodic survey inspection data is available for multiple years, as well as other data such as the method of construction of the tunnel. Expert annotations are also available for each 10m tunnel segment for each survey as to the degree of repair urgency which are used for both training and model evaluation. We show that good predictive power can be obtained and discuss the relative merits of a number of learning methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Funding Information: | Funder Grant number EU - European Union 280712 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jul 2016 11:31 |
Last Modified: | 22 Jan 2018 14:12 |
Published Version: | https://doi.org/10.3233/978-1-61499-672-9-1769 |
Status: | Published |
Publisher: | IOS Press |
Series Name: | Frontiers in Artificial Intelligence and Applications |
Identification Number: | 10.3233/978-1-61499-672-9-1769 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103053 |