Russian Federation
Russian Federation
UDC 614.841
The problem of cascading fire and explosion hazards at rail infrastructure facilities transporting flammable liquids is considered. It is shown that depressurization of a tank car carrying AI-95 gasoline in conditions of rolling stock accumulation on shunting tracks can initiate a chain reaction of fires in adjacent objects. A Markov chain of emergency development is constructed, including nine system states – from normal operation to catastrophic consequences. Analytical expressions and numerical estimates of the state probabilities are obtained. It is established that, for given transition rates, the probability of a cascading fire scenario occurring by 50 min. is 97,75 %. The results of the study can be used to justify organizational and technical measures to minimize damage and improve fire safety at railway facilities.
railway transport, transportation of dangerous goods, fire, explosion, cascade process
1. Tarancev A.A., Kozhevin D.F., Potashev D.A. Markovskaya model' kaskadnogo razvitiya pozharovzryvoopasnoj situacii na avtostoyanke // Nauchno-analiticheskij zhurnal «Vestnik Sankt-Peterburgskogo universiteta Gosudarstvennoj protivopozharnoj sluzhby MCHS Rossii». 2023. № 4. S. 16–25. DOI:https://doi.org/10.61260/2218-130H-2023-4-16-25
2. O modelirovanii kaskadnogo razvitiya chrezvychajnyh situacij pri pozharah na podzemnyh avtostoyankah / A.A. Tarancev [i dr.] // Problemy upravleniya riskami v tekhnosfere. 2023. № 3 (67). S. 131–140. DOI:https://doi.org/10.61260/1998-8990-2023-3-131-140
3. Tarancev A.A., Marinov M.L., Konovalov I.N. O modelirovaniya ekologicheskoj i pozharnoj bezopasnosti morskih neftyanyh terminalov cepyami Markova // Morskie intellektual'nye tekhnologii. 2024. № 3. S. 339–346. DOI:https://doi.org/10.37220/MIT.2024.65.3.059
4. Tarancev A.A., Marinov M.L., Konovalov I.N. O sposobe modelirovaniya pozharovzryvoopasnyh situacij na paromah i plavuchih avtostoyankah s ispol'zovaniem cepej Markova // Morskie intellektual'nye tekhnologii. 2024. № 1. S. 270–275. DOI:https://doi.org/10.37220/MIT.2024.63.1.032
5. O modelirovaniya pozharoopasnoj situacii na sudne s ispol'zovaniem cepej Markova / A.A. Tarancev [i dr.] // Morskie intellektual'nye tekhnologii. 2026. № 1. S. 320–326. DOI:https://doi.org/10.37220/MIT.2026.71.1.033
6. Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating / C. Hoelzl [et al.] // Journal of Physics: Conference Series. 2024. № 2647. P. 182024. DOI:https://doi.org/10.1088/1742-6596/2647/18/182024
7. Yan T.-H., De Almeida Costa M., Corman F. Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data // Transportation Research Record. 2023. № 2677 (6). P. 708–719. DOI:https://doi.org/10.1177/03611981221150245
8. Forecasting the state of technogenic emergency situation on the railway transport using data mining technologies / T. Savchuk [et al.] // Przegląd Elektrotechniczny. 2014. № 90 (1). P. 50–53. DOI:https://doi.org/10.12915/pe.2014.01.12
9. Kanaev A.K., Alekseev A.I. Imitacionnaya model' polumarkovskogo tipa dlya issledovaniya nadezhnosti ustrojstv TSN pri rabote v setyah svyazi zheleznodorozhnogo transporta // Trudy uchebnyh zavedenij svyazi. 2025. T. 11. № 6. S. 43–52. DOI:https://doi.org/10.31854/1813-324X-2025-11-6-43-52 EDN:CFAOSQ
10. Prus M.Yu. Stohasticheskoe modelirovanie kaskadnyh scenariev vozniknoveniya i razvitiya chrezvychajnyh situacij // Tekhnologii tekhnosfernoj bezopasnosti. 2022. № 1 (95). S. 170–195. DOI:https://doi.org/10.25257/TTS.2022.5.95.170-195
11. Primenenie mashinoobuchaemyh cepej Markova dlya opredeleniya ranga pozhara i prognozirovaniya faz ego razvitiya / N.G. Topol'skij [i dr.] // Pozharovzryvobezopasnost'. 2021. T. 30. № 6. S. 39–51. DOI:https://doi.org/10.22227/0869 7493.2021.30.06.39-51
12. Samigullin G.H., Evloev Z.B. Chrezvychajnye situacii na ob"ektah proizvodstva i hraneniya szhizhennogo prirodnogo gaza // Bezopasnost' zhiznedeyatel'nosti. 2026. № 2. S. 48–51.
13. Tanklevskij L.T., Zybina O.A., Tarancev A.A. Primenenie markovskih cepej k zadacham podderzhaniya gotovnosti organizacionnyh i tekhnicheskih sistem // XXI vek: itogi proshlogo i problemy nastoyashchego plyus. 2023. T. 12. № 2 (62). S. 26–34. EDN: https://elibrary.ru/NUZAGG
14. Tanklevskij L.T., Tarancev A.A., Babikov I.A. Metod upravleniya podderzhaniem gotovnosti sredstv protivopozharnoj zashchity s ispol'zovaniem Markovskih cepej // Nauchno-analiticheskij zhurnal «Vestnik Sankt-Peterburgskogo universiteta Gosudarstvennoj protivopozharnoj sluzhby MCHS Rossii». 2022. № 4. S. 60–69.
15. Review of Probabilistic Modeling of Pavement Performance Using Markov Chains / M.S. Yamany [et al.] // Preprints. 2024. DOI:https://doi.org/10.20944/preprints202407.1863.v1
16. Samigullin G.H., Tregub N.A., Ivahnyuk G.K. Ocenka veroyatnosti eskalacii pozhara na ob"ektah infrastruktury zheleznodorozhnogo transporta // Problemy upravleniya riskami v tekhnosfere. 2025. № 3 (75). S. 87–98. DOI:https://doi.org/10.61260/1998-8990-2025-3-87-98
17. Ventcel' E.S. Issledovanie operacij. M.: Sovetskoe radio, 1972. 552 s.
18. Butyrskij E.Yu., Matveev A.V. Matematicheskoe modelirovanie sistem i processov. SPb.: Strategiya budushchego, 2022. 733 s. DOI:https://doi.org/10.37468/book_011222 EDN: https://elibrary.ru/CCRIRT
19. Kolmogorov A.N. Teoriya veroyatnostej i matematicheskaya statistika. M.: Nauka, 1986. 534 s.
20. Matveev A.V. Metody modelirovaniya i prognozirovaniya. SPb.: S.-Peterb. un-t GPS MCHS Rossii, 2022. 230 s. EDN: https://elibrary.ru/IMLKWS
