SYSTEM OF INDIVIDUAL, COLLECTIVE, AND SOCIAL RISK INDICATORS FOR SPECIAL PURPOSE FACILITIES

SYSTEM OF INDIVIDUAL, COLLECTIVE, AND SOCIAL RISK INDICATORS FOR SPECIAL PURPOSE FACILITIES

Authors

DOI:

https://doi.org/10.36074/grail-of-science.01.05.2026.072

Keywords:

individual risk, collective risk, social risk, risk indicators, special purpose facilities, emergency conditions, protection zones

Summary

This study develops an integrated system of individual, collective, and social risk indicators for special purpose facilities under emergency conditions. The proposed approach is based on the assumption that risk at such facilities cannot be represented adequately by a single generalized measure, because hazardous events affect exposed individuals, personnel groups, and concentrated operational structures in different ways and with different consequences. The framework differentiates the analytical role of each indicator and links them to protection zones, personnel distribution, and decision support tasks. Individual risk is used for person level exposure assessment, collective risk for group consequence analysis within specific zones, and social risk for high consequence scenarios involving simultaneous losses or severe operational disruption. The results show that the integrated use of these indicators provides a more rigorous and operationally relevant basis for risk assessment than undifferentiated models. The proposed system improves the analytical interpretation of emergency conditions and supports more justified protective decisions at special purpose facilities.

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References

Akhundov, R., & Hashimov, E. (2026). Enhancing the physical protection of critical facilities through the integration of physical process models and machine learning. Grail of Science, 61, 722–731. https://doi.org/10.36074/grail-of-science.23.01.2026.083 DOI: https://doi.org/10.36074/grail-of-science.23.01.2026.083

Talibov, A. et al. (2026). Integral sensitivity indicator based on multilayer indicators for critical infrastructure and special-purpose facilities and the selection of measures. Grail of Science, (65). https://doi.org/10.36074/grail-of-science.03.04.2026.063 DOI: https://doi.org/10.36074/grail-of-science.03.04.2026.063

Akhundov, R., & Hashimov, E. (2026). Modeling information processes and deriving measurable requirements in physical protection system design. Management Information System and Devices, 1(188), 5–16. https://doi.org/10.30837/0135-1710.2026.188.005 DOI: https://doi.org/10.30837/0135-1710.2026.188.005

Akhundov, R., Talibov, A., & Hashimov, E. (2026). Information processes in the conceptual design of physical protection systems. Grail of Science, (65), 655–671. https://doi.org/10.36074/grail-of-science.03.04.2026.073 DOI: https://doi.org/10.36074/grail-of-science.03.04.2026.073

Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references, and DEA-Solver software. Kluwer Academic Publishers. DOI: https://doi.org/10.1007/b109347

Yang, J., Huang, L., Ma, H., Xu, Z., Yang, M., & Guo, S. (2022). A 2D-graph model-based heuristic approach to visual backtracking security vulnerabilities in physical protection systems. International Journal of Critical Infrastructure Protection, 38, Article 100554. https://doi.org/10.1016/j.ijcip.2022.100554 DOI: https://doi.org/10.1016/j.ijcip.2022.100554

Zou, B., Yang, M., Zhang, Y., Benjamin, E.-R., Tan, K., Wu, W., & Yoshikawa, H. (2018). Evaluation of vulnerable path: Using heuristic path-finding algorithm in physical protection system of nuclear power plant. International Journal of Critical Infrastructure Protection, 23, 90–99. https://doi.org/10.1016/j.ijcip.2018.08.006 DOI: https://doi.org/10.1016/j.ijcip.2018.08.006

Akhundov, R., & Hashimov, E. G. (2025). Quantitative categorization of facilities and modeling of potential adversaries. Grail of Science, 60, 469–482. https://doi.org/10.36074/grail-of-science.26.12.2025.049 DOI: https://doi.org/10.36074/grail-of-science.26.12.2025.049

Reniers, G. L. L., & Audenaert, A. (2014). Preparing for major terrorist attacks against chemical clusters: Intelligently planning protection measures with respect to domino effects. Process Safety and Environmental Protection, 92(6), 583–589. https://doi.org/10.1016/j.psep.2013.04.002 DOI: https://doi.org/10.1016/j.psep.2013.04.002

Řehák, D., Senovsky, P., Hromada, M., & Lovecek, T. (2019). Complex approach to assessing resilience of critical infrastructure elements. International Journal of Critical Infrastructure Protection, 25, 125–138. https://doi.org/10.1016/j.ijcip.2019.03.003 DOI: https://doi.org/10.1016/j.ijcip.2019.03.003

Cozens, P., & Love, T. (2015). A review and current status of crime prevention through environmental design (CPTED). Journal of Planning Literature, 30(4), 393–412. https://doi.org/10.1177/0885412215595440 DOI: https://doi.org/10.1177/0885412215595440

El Wely, I. C., & Chetaine, A. (2020). Analysis of physical protection system effectiveness of nuclear power plants based on performance approach. Annals of Nuclear Energy, 153. https://doi.org/10.1016/j.anucene.2020.107980 DOI: https://doi.org/10.1016/j.anucene.2020.107980

Garcia, M. L. (2008). Design and evaluation of physical protection systems (2nd ed.). Elsevier. https://doi.org/10.1016/C2009-0-25612-1 DOI: https://doi.org/10.1016/B978-0-08-055428-0.50005-1

Babayev, S. et al. (2026). Conceptual-theoretical foundations and the transformative role of artificial intelligence in multidomain operations. Grail of Science, 64, 606–615. https://doi.org/10.36074/grail-of-science.20.03.2026.069 DOI: https://doi.org/10.36074/grail-of-science.20.03.2026.069

Gündüz, M. Z., & Daş, R. (2020). Akıllı şebekelerde iletişim altyapısı ve siber güvenlik. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(2), 970–984. https://doi.org/10.21597/jist.655990 DOI: https://doi.org/10.21597/jist.655990

Akhundov, R., Talibov, A., & Hashimov, E. (2026). A probabilistic approach to risk formalization in physical protection systems for special purpose facilities. Grail of Science, 66, 456–475. https://doi.org/10.36074/grail-of-science.17.04.2026.051 DOI: https://doi.org/10.36074/grail-of-science.17.04.2026.051

Kaplan, S., & Garrick, B. J. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 11–27. https://doi.org/10.1111/j.1539-6924.1981.tb01350.x DOI: https://doi.org/10.1111/j.1539-6924.1981.tb01350.x

Kostin, V., & Borovsky, A. (2020). Definition of basic violators for critically important objects using the information probability method and cluster analysis. In CEUR Workshop Proceedings (Vol. 2667, pp. 343–347). 6th International Conference Information Technology and Nanotechnology, Session Data Science, ITNT-DS 2020, Samara, Russian Federation.

Hashimov, E., Akhundov, R. G., Talibov, A. M., & Islamov, I. (2026). Constrained optimization of an integral security indicator for adaptive management of hazardous facilities. Grail of Science, 62, 1003–1014. https://doi.org/10.36074/grail-of-science.20.02.2026.109 DOI: https://doi.org/10.36074/grail-of-science.20.02.2026.109

Hashimov, E., et al. (2026). Research of the efficiency multiservice networks using MIMO technology. Advanced Information Systems, 10(1), 66–71. https://doi.org/10.20998/2522-9052.2026.1.08 DOI: https://doi.org/10.20998/2522-9052.2026.1.08

Kampova, K., Lovecek, T., & Řehák, D. (2020). Quantitative approach to physical protection systems assessment of critical infrastructure elements: Use case in the Slovak Republic. International Journal of Critical Infrastructure Protection, 30, Article 100376. https://doi.org/10.1016/j.ijcip.2020.100376 DOI: https://doi.org/10.1016/j.ijcip.2020.100376

Lovecek, T., Ristvej, J., & Simak, L. (2010). Critical infrastructure protection systems effectiveness evaluation. Journal of Homeland Security and Emergency Management, 7(1). https://doi.org/10.2202/1547-7355.1613 DOI: https://doi.org/10.2202/1547-7355.1613

Mondal, S., Adak, B., & Mukhopadhyay, S. (2023). Functional and smart textiles for military and defence applications. In Smart and functional textiles (p. 397). DOI: https://doi.org/10.1515/9783110759747-011

Rehak, D., Slivkova, S., Janeckova, H., Stuberova, D., & Hromada, M. (2022). Strengthening resilience in the energy critical infrastructure: Methodological overview. Energies, 15(14), Article 5276. https://doi.org/10.3390/en15145276 DOI: https://doi.org/10.3390/en15145276

Shoop, B., et al. (2006). Mobile detection assessment and response systems (MDARS): A force protection physical security operational success. In Unmanned Systems Technology VIII (Vol. 6230, pp. 668–678). SPIE. DOI: https://doi.org/10.1117/12.665939

Author Biographies

Ramil Akhundov, National Defense University, Baku, Azerbaijan

PhD in National Security and Military Sciences

Aziz Talibov, Azerbaijan Technical University, Baku, Azerbaijan

ScD in National Security and Military Sciences

Elshan Hashimov, Azerbaijan Technical University, Baku, Azerbaijan

ScD in National Security and Military Sciences

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Published

01.05.2026

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How to Cite

Akhundov, R., Talibov, A., & Hashimov, E. (2026). SYSTEM OF INDIVIDUAL, COLLECTIVE, AND SOCIAL RISK INDICATORS FOR SPECIAL PURPOSE FACILITIES. Grail of Science, (67), 635–647. https://doi.org/10.36074/grail-of-science.01.05.2026.072

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Military sciences, National security and Security of the State Border

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