Quantifying net benefits of intervention programmes to enable their digitalised generation
Open access
Date
2021-09Type
- Journal Article
Abstract
Interventions on infrastructure networks are required to ensure that they continue to provide the service expected of them. Although all existing methods for determining intervention programmes take into consideration, in some form, the costs and benefits associated with the interventions, there is a wide variation in exactly how it is done. With increasing strides to digitalise the determination of optimal intervention programmes, a systematic and quantitative method for determining their net benefits is required. The novelty in the proposed method is how the provided service and intervention costs over time are considered when determining optimal intervention programmes. It considers the difference between candidate intervention programmes and a reference intervention programme. In other words, it makes clear whether network-level considerations - for example, network-level synergies and constraints - should result in the intervention on an asset being executed earlier or later than indicated by the optimal asset life cycle. The method enables the use of advanced operations research algorithms in infrastructure-management systems and, therefore, helps enable the automated generation of optimal intervention programmes. For illustration, the method is used in the determination of the optimal intervention programme for a small railway network. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000506666Publication status
publishedExternal links
Journal / series
Infrastructure Asset ManagementVolume
Pages / Article No.
Publisher
TelfordSubject
infrastructure planning; management; planning & schedulingOrganisational unit
03859 - Adey, Bryan T. / Adey, Bryan T.
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
03859 - Adey, Bryan T. / Adey, Bryan T.
Funding
769373 - Future proofing strategies FOr RESilient transport networks against Ectreme Events (EC)
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