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Systems and reliability modelling for probabilistic risk assessment of improvised explosive device attacks

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posted on 2025-05-11, 13:42 authored by Matthew Grant
Improvised Explosive Devices, commonly known as IEDs, inspire terror in communities across the globe, and continue to be a terrorist weapon of choice. IED Attacks target infrastructure and population centres, including airports, buildings and sporting venues using a broad array of devices, from vehicle borne IEDs to small postal devices. They provide the media with sensational headlines and governments with tough challenges, balancing the electorate’s emotive needs against investing in projects on the basis of rigorous Cost-Benefit Analysis. With increasing pressures on the economies of nations, spending on counter-terrorism is subject to greater scrutiny. Homeland security agencies are no longer exempt from government fiscal due diligence, needing to justify how their spending achieves best value-for-money. Probabilistic Risk Assessment (PRA) is a valuable tool that can assist in this endeavour. A significant issue surrounding justification of counter-terrorism spending lies in how risk and cost-benefit studies are conducted. Much work in the counter-terrorism domain is informed by expert opinion and traditional risk analysis techniques. These approaches have been criticised, particularly because they do not account for terrorists as being adaptive or intelligent adversaries. This research develops a multi-disciplinary approach to PRA for IED Attack, based on a fusion of systems and military engineering techniques. This research has identified that IED Attacks can be characterised by a combination of human factors and reliability modeling techniques to quantify many of the aspects surrounding IED Attacks, and resolve some of the key challenges surrounding the use of PRA to assess the risks associated with terrorism. The PRA model that has been developed can be used to assess the risk-reduction associated with IED Attack countermeasures, and used to complement other risk assessment modelling techniques.

History

Year awarded

2018.0

Thesis category

  • Doctoral Degree

Degree

Doctor of Philosophy (PhD)

Supervisors

Stewart, Mark (University of Newcastle)

Language

  • en, English

College/Research Centre

Faculty of Engineering and Built Environment

School

School of Engineering

Rights statement

Copyright 2018 Matthew Grant

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