A. Dychko, L. Demchuk, L. Kryukovskaya, A. Kagukina, I. Belmega. Methodological foundations of intelligent forecasting of fire safety for critical industrial infrastructure facilities. P. 340-355.

Methodological foundations of intelligent forecasting of fire safety for critical industrial infrastructure facilities

 

Dychko Alina

National Transport University

https://orcid.org/0000-0003-4632-3203

 

Demchuk Liudmyla

Zhytomyr Polytechnic State University

https://orcid.org/0000-0001-5698-7113

 

Kryukovskaya Lesіa

National Transport University

https://orcid.org/0000-0001-8944-8036

 

Kahukina Anastasiia

Zhytomyr Polytechnic State University

https://orcid.org/0000-0001-8932-1211

 

Belmega Ivan

Zhytomyr Polytechnic State University

https://orcid.org/0009-0007-2524-6217

 

DOI: https://doi.org/10.52363/2524-0226-2026-43-21

 

Keywords: intelligent forecasting, pyrogenic safety, critical infrastructure facilities, man-made load, hazard monitoring

 

Аnnotation

 

Substantiates new methodological foundations for the intelligent forecasting of pyrogenic (fire) safety of critical industrial infrastructure facilities under conditions of dynamic man-made and exter-nal threats. The relevance of the research stems from the need to transition from reactive fire suppres-sion to predictive risk management using artificial intelligence technologies. A comprehensive meth-odological concept has been developed that integrates methods of systems analysis, catastrophe theo-ry, and machine learning. An architecture for an intelligent system for early detection and forecasting of fire development dynamics has been developed, capable of assessing the probability of pyrogenic threats with an accuracy of 92–95 %. Mathematical models of the nonlinear propagation of heat flux-es in enclosed spaces of complex industrial facilities have been obtained, taking into account the spe-cifics of the fire load. Decision support algorithms have been developed for operational personnel that minimize response time to threats by 30–40 % and automatically generate scenarios for localizing the source of ignition. The methodology is based on a comprehensive approach. The empirical basis was formed using statistical analysis of historical data on fires at industrial facilities in recent years. Math-ematical modeling of combustion and heat and mass transfer processes was implemented using com-putational fluid dynamics methods. The intelligent component was developed by designing and train-ing ensembles of neural networks (in particular, LSTM for time series) on synthetic and real sensor monitoring data samples (temperature, gas concentration, smoke density). The adequacy of the mod-els was verified through a computer simulation experiment and by comparing the results with known expert scenarios of man-made accident development.

 

References

 

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Received by the editorial board: 10.03.2026

Accepted for publication: 13.04.2026

Date of publication (release): 31.05.2026

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  • A. Dychko, L. Demchuk, L. Kryukovskaya, A. Kagukina, I. Belmega. Methodological foundations of intelligent forecasting of fire safety for critical industrial infrastructure facilities. P. 340-355.