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Airblast variability and fatality risks from a VBIED in a complex urban environment

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  • Marks, Nicholas A
  • Stewart, Mark G.
  • Netherton, Michael D.
  • Stirling, Chris G.

Abstract

Explosive blasts and prediction of fatality risks in urban environments is a complicated task due to the variability in blast wave reflection and propagation. The terrorist threats considered in this paper are vehicle-borne improvised explosive devices (VBIED) containing 225 kg or 450 kg of TNT or ammonium nitrate fuel oil (ANFO) detonated in an open street. This paper uses Viper::Blast CFD software to estimate the variability of explosive blast loads using Monte-Carlo sampling. To probabilistically model the blast wave, the paper takes into consideration the variability of explosive charge mass, detonation location, height of detonation, net equivalent quantity, atmospheric pressure and temperature, and model errors. The fatality risk assessment combines lung-rupture, whole-body displacement and skull fracture dependant on the pressure and impulse. It was found that the mean fatality risk for a 450 kg home-made ANFO explosive device detonated at a road T-intersection is 16% for people exposed in the street. If bollards were placed 10 m from the main street then fatality risk for people in the main street is reduced by over 90%. It was found that a deterministic analysis yielded fatality risks 10–60% higher than a probabilistic analysis, leading to an overly conservative assessment of safety risks.

Suggested Citation

  • Marks, Nicholas A & Stewart, Mark G. & Netherton, Michael D. & Stirling, Chris G., 2021. "Airblast variability and fatality risks from a VBIED in a complex urban environment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000272
    DOI: 10.1016/j.ress.2021.107459
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    References listed on IDEAS

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    1. Stewart, Mark G. & Netherton, Michael D., 2008. "Security risks and probabilistic risk assessment of glazing subject to explosive blast loading," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 627-638.
    2. Russo, Paola & Parisi, Fulvio, 2016. "Risk-targeted safety distance of reinforced concrete buildings from natural-gas transmission pipelines," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 57-66.
    3. Richard G. Little, 2007. "Cost-effective Strategies to Address Urban Terrorism: A Risk Management Approach," Chapters, in: Harry W Richardson & Peter Gordon & James E. Moore II (ed.), The Economic Costs and Consequences of Terrorism, chapter 5, Edward Elgar Publishing.
    4. Thöns, Sebastian & Stewart, Mark G., 2019. "On decision optimality of terrorism risk mitigation measures for iconic bridges," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 574-583.
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    Cited by:

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    2. Kishore, Katchalla Bala & Gangolu, Jaswanth & Ramancha, Mukesh K. & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    3. Nguyen, Hoang & Bui, Xuan-Nam & Topal, Erkan, 2023. "Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Kapoor, Medha & Christensen, Christian Overgaard & Schmidt, Jacob Wittrup & Sørensen, John Dalsgaard & Thöns, Sebastian, 2023. "Decision analytic approach for the reclassification of concrete bridges by using elastic limit information from proof loading," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Li, Qilin & Wang, Yang & Chen, Wensu & Li, Ling & Hao, Hong, 2024. "Machine learning prediction of BLEVE loading with graph neural networks," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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