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Casualty Risks Induced by Primary Fragmentation Hazards from High-explosive munitions

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  • Qin, Hao
  • Stewart, Mark G.

Abstract

Fatalities and injuries are mainly attributed to primary fragmentation if accidental or malevolent detonation of high-explosive munitions occurs in an open space. This study aims to develop a simulation-based approach to assess individual casualty risks from primary fragments naturally generated by detonation of high-explosive munitions, which enables a stochastic characterization of fragment generation, trajectories, modelling uncertainties, and human vulnerability. The proposed method is demonstrated by a numerical example estimating the fatality and injury risks for an individual in a standing position exposed to the detonation of a single 105 mm projectile. The results suggest that, as expected, the individual fatality and injury risks decrease with an increasing stand-off distance. At a stand-off distance greater than 40 m, an individual is more likely to suffer injuries rather than fatality. The safety distance obtained from the present study is 97 m which is close to but less conservative than a safety distance of 104 m in existing literature and standards.

Suggested Citation

  • Qin, Hao & Stewart, Mark G., 2021. "Casualty Risks Induced by Primary Fragmentation Hazards from High-explosive munitions," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003938
    DOI: 10.1016/j.ress.2021.107874
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    References listed on IDEAS

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    1. Häring, I. & Schönherr, M. & Richter, C., 2009. "Quantitative hazard and risk analysis for fragments of high-explosive shells in air," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1461-1470.
    2. Stewart, Mark G. & Netherton, Michael D., 2019. "A probabilistic risk-acceptance model for assessing blast and fragmentation safety hazards," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
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    Cited by:

    1. 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).
    2. Iaiani, Matteo & Sorichetti, Riccardo & Tugnoli, Alessandro & Cozzani, Valerio, 2024. "Modelling standoff distances to prevent escalation in shooting attacks to tanks storing hazardous materials," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Si, Doudou & Pan, Zuanfeng & Zhang, Haipeng, 2024. "Probabilistic assessment and expression of load factor design model for explosive blast loading," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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