Using CWA to Understand and Enhance Infrastructure Resilience

Walker, Guy H.; Beevers, Lindsay and Strathie, Ailsa (2017). Using CWA to Understand and Enhance Infrastructure Resilience. In: Stanton, Neville A.; Salmon, Paul M.; Walker, Guy H. and Jenkins, Daniel P. eds. Cognitive Work Analysis: Applications, Extensions and Future Directions. CRC Press, pp. 403–418.



A nation’s critical infrastructure needs to be able to withstand disturbances as they happen, and bounce back afterward. Most nations have the equivalent of a National Risk Register (e.g. Cabinet Office, 2013). In the United Kingdom it presents a range of civil emergencies with a greater than 1 in 20 chance of occurring in the next 5 years, and with the potential to yield impacts ranging from social disruption and economic harm through to widespread illness and fatalities. Flooding is a prime example (p. 10). The ability of daily life to continue in the face of disturbances like this does not depend on a single engineering solution, rather, on the ability of organisations, infrastructures and individuals to anticipate the changing shape of risk before failures and harm occur, then to respond in effective ways when it does. This chapter describes how the latest research on flood vulnerability was put in touch with CWA, specifically the first phase (work domain analysis/abstraction hierarchy [AH]), enabling this wider view to be captured explicitly. Several real towns were modelled and subject to a simulated 1 in 200 year flood event. The method shows how critical functions and processes at higher levels of system abstraction are progressively degraded as individual ‘physical objects’, and at low levels of abstraction, are knocked out. In addition, network metrics are extracted from the AH to enable each town to be characterised in terms of its vulnerability and positioned in a universal ‘vulnerability space’. Solutions for improving resilience vary depending on what region of the space is occupied, and the method can be deployed to determine this for any town in any region of the world.

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