An ontological approach for pathology assessment and diagnosis of tunnels
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4644
- Type
- D - Journal article
- DOI
-
10.1016/j.engappai.2019.103450
- Title of journal
- Engineering Applications of Artificial Intelligence
- Article number
- 103450
- First page
- -
- Volume
- 90
- Issue
- -
- ISSN
- 0952-1976
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://doi.org/10.1016/j.engappai.2019.103450
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Addresses a timely (€10M EU-project:NETTUN) interdisciplinary challenge: expressing expert knowledge to a fit-for-purpose ontological model to assist maintaining ageing infrastructure. Unique in the application of the METHONTOLOGY methodology to produce world’s first linear-transport infrastructure ontologies. Successful validation on real SNCF/Swiss-Rail data promises impact on sustainability, cost savings, training. Subsequent ECAI-PAIS-2018 paper learns repair urgency. Extends “best in-use” ESWC-15 paper (which inspired e.g. https://doi.org/10.1016/j.autcon.2019.102929) with rigorous industrial evaluation, and systematic methodology description facilitating adoption in other domains: fault-modelling for decision support (DST:iCASE); streetworks DSS(https://doi.org/10.1016/j.eswa.2020.113461); Highways England(Phillip.Proctor@highwaysengland.co.uk) thin-surface paving project; DSTL-funded OPIS project(s.marshall@fnc.co.uk); DSTL EPSRC-CASE Phd-studentship on safety-analysis(Glen Hart: gkhart@mail.dstl.gov.uk). CTTU-20(Melbourne) keynote(Cohn).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
