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Infrastructure Resilience Conference 2018

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NISMOD-DB: A National Infrastructure Modelling Database for Interdependent Infrastructure Systems Planning and Network Resilience Assessment

Reliable, consistent and detailed spatial-temporal data, information and knowledge on infrastructure systems performance, network dynamics and asset condition are essential to inform both the short term assessment and longer term planning of infrastructure system resilience. However, increasingly it is simply not sufficient to have detailed knowledge on the state of a single infrastructure system or a single sector as the interdependencies between them have been recognised as playing a critical role in understanding their true capability to meet demand in a resilient manner. Interdependencies play an important role across a range of infrastructure scales, between sectors, systems, networks and levels of governance. The recognition of these complex multiple-levels of interdependent interactions within infrastructure has led to the development of new system-of-systems approaches to planning system delivery and also assessing network robustness and resilience. However, not only are new approaches required for analysing the interdependencies between infrastructures in a system-of-systems manner, but this needs to be matched by new innovative database tools that can explicitly construct, store, manage and retrieve interdependencies.

In this paper we present the work undertaken within the UK Infrastructure Transitions Research Consortium (ITRC) to develop a national scale geospatial database for infrastructure system and network analytics and modelling; NISMOD-DB (National Infrastructure Systems Modelling Database). Within NISMOD-DB a suite of new database models have been built on top of the spatial data handling capabilities of a spatial database. These database models allow the explicit geo-temporal parameterisation of the interdependencies that exist between different infrastructure sectors which are employed within a system-of-systems model for long-term infrastructure planning called NISMOD-LP. NISMOD-DB also contains new database models for the representation spatially and topologically of critical infrastructure networks and the interdependencies between them for coupled interdependent fragility and vulnerability analysis and modelling. Via several examples, we demonstrate the capability of NISMOD-DB to inform long-term resilience analysis of infrastructure systems and also undertake infrastructure network risk and robustness analysis on individual, as well as spatially coupled interdependent infrastructure networks.

Stuart Barr
Newcastle University
United Kingdom

Craig Robson
Newcastle University
United Kingdom

Matthew Ives
Oxford University
United Kingdom

Jim Hall
Oxford University
United Kingdom

Scott Thacker
Oxford University
United Kingdom

 

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