Prediction of gas explosion consequences in residential buildings based on artificial neural network

HU Qianran; SHEN Xingyu; ZHANG Qi; YUAN Mengqi; FAN Wulong; WANG Jizhe; YANG Huijie; LIN Rui

doi:10.11883/bzycj-2025-0382

Volume 46 Issue 5

May 2026

Turn off MathJax

Article Contents

Article Navigation > Explosion And Shock Waves > 2026 > 46(5): 051445

HU Qianran, SHEN Xingyu, ZHANG Qi, YUAN Mengqi, FAN Wulong, WANG Jizhe, YANG Huijie, LIN Rui. Prediction of gas explosion consequences in residential buildings based on artificial neural network[J]. Explosion And Shock Waves, 2026, 46(5): 051445. doi: 10.11883/bzycj-2025-0382

Citation:

HU Qianran, SHEN Xingyu, ZHANG Qi, YUAN Mengqi, FAN Wulong, WANG Jizhe, YANG Huijie, LIN Rui. Prediction of gas explosion consequences in residential buildings based on artificial neural network[J]. Explosion And Shock Waves, 2026, 46(5): 051445. doi: 10.11883/bzycj-2025-0382

Citation:

HU Qianran, SHEN Xingyu, ZHANG Qi, YUAN Mengqi, FAN Wulong, WANG Jizhe, YANG Huijie, LIN Rui. Prediction of gas explosion consequences in residential buildings based on artificial neural network[J]. Explosion And Shock Waves, 2026, 46(5): 051445. doi: 10.11883/bzycj-2025-0382

PDF( 6701 KB)

Prediction of gas explosion consequences in residential buildings based on artificial neural network

doi: 10.11883/bzycj-2025-0382

HU Qianran^1
,,
SHEN Xingyu²,
ZHANG Qi^{1, 3},
YUAN Mengqi^{1, 4
,
,},
FAN Wulong⁵,
WANG Jizhe¹,
YANG Huijie¹,
LIN Rui¹

1.
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, China
2.
China Center for Information Industry Development, Beijing 100048, China
3.
College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
4.
Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 400044, China
5.
Institute of Forensic Science Ministry of Public Security, Beijing 100038, China

Received Date: 2025-11-24
Rev Recd Date: 2026-02-05

Available Online: 2026-02-09

Publish Date: 2026-05-05

Abstract

Abstract

Addressing the challenge of highly nonlinear evolution and the difficulty in accurately predicting the consequences of residential gas explosion accidents, a data-driven investigation into gas explosion consequence prediction was conducted. An artificial neural network-based prediction method for explosion accident consequences was proposed. By large-scale numerical simulations, a gas explosion consequence dataset covering various residential unit layouts was generated. Through sensitivity analysis and accuracy validation, an intelligent prediction model for gas explosion consequences was ultimately established. The model achieves prediction errors below 15% and 5% for indoor peak overpressure and temperature, respectively, while the maximum error in predicting spatial location coordinates remains within 25%. This enables batch prediction of the most severe indoor explosion consequences and their spatial locations for arbitrary ignition positions across different residential unit layouts. The results indicate that as unit area increases and spatial layout becomes progressively more complex, the peak overpressure and temperature values correspondingly increase. The living room consistently exhibits the lowest overpressure levels, whereas areas near windowless bedroom walls tend to form extreme overpressure and temperature zones. Ignition in the kitchen and bedroom leads to the most severe indoor overpressure and temperature consequences, respectively, reflecting the differential impact patterns of ignition location on explosion outcomes. The findings provide important references for expanding the predictive application of artificial intelligence in the field of gas explosions and for the efficient prevention and control of explosion accidents.
- residential buildings,
- gas explosion,
- artificial neural network,
- consequence prediction,
- explosion overpressure

FullText(HTML)

References(32)

References

[1]	楚紫涵, 张云, 安文鑫, 等. 燃料浓度对受限空间内氢气/空气预混气体爆炸特性的影响 [J/OL]. 爆炸与冲击, 2026, 5. https://link.cnki.net/urlid/51.1148.o3.20250909.1135.010. CHU Z H, ZHANG Y, AN W X, et al. The influence of fuel concentration on the explosion dynamics characteristics of hydrogen/air premixed gas in confined spaces [J/OL]. Explosion and Shock Waves, 2026, 5. https://link.cnki.net/urlid/51.1148.o3.20250909.1135.010.
[2]	LI F, DUAN B Y, ZHANG Y, et al. Post-risk assessment model for gas explosion accidents based on the coupling effect of disaster-causing factors [J]. Reliability Engineering & System Safety, 2026, 266: 111733. DOI: 10.1016/j.ress.2025.111733.
[3]	LI C, PANG L, WANG Z R, et al. Influence of packing structure on the thermal-induced self-ignition of AlMg alloy powder [J]. Powder Technology, 2026, 468: 121669. DOI: 10.1016/j.powtec.2025.121669.
[4]	ZHANG Q, QIAN X, LI R, et al. Explosion characteristics and chemical kinetics of blended LPG/DME clean fuel based on pyrolysis and oxidation mechanism model [J]. Fuel, 2022, 320: 123896. DOI: 10.1016/j.fuel.2022.123896.
[5]	MOLKOV V V, MAKAROV D V. Rethinking the physics of a large-scale vented explosion and its mitigation [J]. Process Safety and Environmental Protection, 2006, 84(1): 33–39. DOI: 10.1205/psep.04232.
[6]	RASBASH D J. The relief of gas and vapour explosions in domestic structures [J]. Fire Safety Science, 1969, 759: 1–10.
[7]	刘振翼, 石世旭, 李明智, 等. 建筑物内非均匀预混天然气泄漏爆炸过程数值模拟 [J]. 安全与环境学报, 2022, 22(5): 2404–2411. DOI: 10.13637/j.issn.1009-6094.2021.0260. LIU Z Y, SHI S X, LI M Z, et al. Numerical simulation of leakage and explosion process of inhomogeneous premixed natural gas in buildings [J]. Journal of Safety and Environment, 2022, 22(5): 2404–2411. DOI: 10.13637/j.issn.1009-6094.2021.0260.
[8]	TONG S, LI X, DING H, et al. Large-scale transient simulation for consequence analysis of hydrogen-doped natural gas leakage and explosion accidents [J]. International Journal of Hydrogen Energy, 2024, 54: 864–877. DOI: 10.1016/j.ijhydene.2023.08.088.
[9]	MA Q, GUO Y, ZHONG M, et al. Numerical simulation of hydrogen explosion characteristics and disaster effects of hydrogen fueling station [J]. International Journal of Hydrogen Energy, 2024, 51: 861–879. DOI: 10.1016/j.ijhydene.2023.05.129.
[10]	HU Q, QIAN X, SHEN X, et al. Investigations on vapor cloud explosion hazards and critical safe reserves of LPG tanks [J]. Journal of Loss Prevention in the Process Industries, 2022, 80: 104904. DOI: 10.1016/j.jlp.2022.104904.
[11]	马梦飞, 於星, 张爱凤, 等. 开放空间高压氢气射流中点火爆炸的实验研究 [J]. 爆炸与冲击, 2024, 44(6): 062101. DOI: 10.11883/bzycj-2023-0037. MA M F, YU X, ZHANG A F, et al. An experimental study on ignition and explosion of high-pressure hydrogen jet in open space [J]. Explosion and Shock Waves, 2024, 44(6): 062101. DOI: 10.11883/bzycj-2023-0037.
[12]	TIAN J W, QI C, PENG K, et al. Improved permeability prediction of porous media by feature selection and machine learning methods comparison [J]. Journal of Computing in Civil Engineering, 2022, 36(2): 04021040. DOI: 10.1061/(ASCE)CP.1943-5487.0000983.
[13]	DAVIES W G, BABAMOHAMMADI S, YANG Y, et al. The rise of the machines: a state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture [J]. Gas Science and Engineering, 2023, 118: 205104. DOI: 10.1016/j.jgsce.2023.205104.
[14]	陈梓薇, 王仲琦, 曾令辉. 基于BP神经网络的爆炸用激波管峰值压力预测方法 [J]. 爆炸与冲击, 2024, 44(5): 054101. DOI: 10.11883/bzycj-2023-0187. CHEN Z W, WANG Z Q, ZENG L H. A method for predicting peak pressure in an explosion shock tube based on BP neural network [J]. Explosion and Shock Waves, 2024, 44(5): 054101. DOI: 10.11883/bzycj-2023-0187.
[15]	XU Y, HUANG Y, MA G. A beetle antennae search improved BP neural network model for predicting multi-factor-based gas explosion pressures [J]. Journal of Loss Prevention in the Process Industries, 2020, 65: 104117. DOI: 10.1016/j.jlp.2020.104117.
[16]	VIANNA S S V, CANT R S. Explosion pressure prediction via polynomial mathematical correlation based on advanced CFD modelling [J]. Journal of Loss Prevention in the Process Industries, 2012, 25(1): 81–89. DOI: 10.1016/j.jlp.2011.07.005.
[17]	LIU L, LIU J, ZHOU Q, et al. An SVR-based machine learning model depicting the propagation of gas explosion disaster hazards [J]. Arabian Journal for Science and Engineering, 2021, 46(10): 10205–10216. DOI: 10.1007/s13369-021-05616-5.
[18]	XU Q, CHEN G, SU S, et al. Prediction of venting gas explosion overpressure based on a combination of explosive theory and machine learning [J]. Expert Systems with Applications, 2023, 234: 121044. DOI: 10.1016/j.eswa.2023.121044.
[19]	IDRIS A M, RUSLI R, MOHAMED M E, et al. Explosion pressure and duration prediction using machine learning: a comparative study using classical models with adam-optimized neural network [J]. Canadian Journal of Chemical Engineering, 2025, 103(1): 137–152. DOI: 10.1002/cjce.25258.
[20]	HEMMATIAN B, CASAL J, PLANAS E, et al. Prediction of BLEVE mechanical energy by implementation of artificial neural network [J]. Journal of Loss Prevention in the Process Industries, 2020, 63: 104021. DOI: 10.1016/j.jlp.2019.104021.
[21]	庞磊, 胡倩然, 马菲菲, 等. 泄爆面特征参数对天然气爆炸超压峰值的影响规律 [J]. 中国安全生产科学技术, 2020, 16(4): 126–131. DOI: 10.11731/j.issn.1673-193x.2020.04.020. PANG L, HU Q R, MA F F, et al. Effect of vent characteristic parameters on overpressure peaks of natural gas explosion [J]. Journal of Safety Science and Technology, 2020, 16(4): 126–131. DOI: 10.11731/j.issn.1673-193x.2020.04.020.
[22]	MOLKOV V V, GRIGORASH A V, EBER R M. Vented gaseous deflagrations: modelling of spring-loaded inertial vent covers [J]. Fire Safety Journal, 2005, 40(4): 307–319. DOI: 10.1016/j.firesaf.2005.01.004.
[23]	JIANG H, CHI M, HOU D, et al. Numerical investigation and analysis of indoor gas explosion: a case study of “6·13” major gas explosion accident in hubei province, china [J]. Journal of Loss Prevention in the Process Industries, 2023, 83: 105045. DOI: 10.1016/j.jlp.2023.105045.
[24]	HU Q R, ZHANG Q, YUAN M Q, et al. Traceability and failure consequences of natural gas explosion accidents based on key investigation technology [J]. Engineering Failure Analysis, 2022, 139: 106448. DOI: 10.1016/j.engfailanal.2022.106448.
[25]	ZHANG Q, WANG Y, LIAN Z. Explosion hazards of LPG-air mixtures in vented enclosure with obstacles [J]. Journal of Hazardous Materials, 2017, 334: 59–67. DOI: 10.1016/j.jhazmat.2017.03.065.
[26]	PANG L, HU Q, ZHAO J, et al. Numerical study of the effects of vent opening time on hydrogen explosions [J]. International Journal of Hydrogen Energy, 2019, 44(29): 15689–15701. DOI: 10.1016/j.ijhydene.2019.04.175.
[27]	VYAZMINA E, JALLAIS S. Validation and recommendations for FLACS CFD and engineering approaches to model hydrogen vented explosions: effects of concentration, obstruction vent area and ignition position [J]. International Journal of Hydrogen Energy, 2016, 41(33): 15101–15109. DOI: 10.1016/j.ijhydene.2016.05.189.
[28]	MA Q, HE Y, GUO Y, et al. Research on the effect of the vent area on the external deflagration process during the explosion vent [J]. Fuel, 2022, 329: 125440. DOI: 10.1016/j.fuel.2022.125440.
[29]	陈晔, 李毅, 李紫婷, 等. 受限空间氢泄爆外部超压特性研究 [J]. 消防科学与技术, 2022, 41(3): 310–315. DOI: 10.3969/j.issn.1009-0029.2022.03.005. CHEN Y, LI Y, LI Z T, et al. Ignition characteristics of finite thick PMMA exposed to thermal radiation [J]. Fire Science and Technology, 2022, 41(3): 310–315. DOI: 10.3969/j.issn.1009-0029.2022.03.005.
[30]	于佳航. 基于机器学习的双燃料动力船舶机舱燃爆事故后果预测方法研究 [D]. 大连: 大连海事大学, 2022. YU J H. Research on consequence prediction method of engine room combustion and explosion accident of dual fuel power ship based on machine learning [D]. DaLian: Dalian Maritime University, 2022.
[31]	SHI J, KHAN F, ZHU Y, et al. Robust data-driven model to study dispersion of vapor cloud in offshore facility [J]. Ocean Engineering, 2018, 161: 98–110. DOI: 10.1016/j.oceaneng.2018.04.098.
[32]	SHI J, LI J, HAO H, et al. Vented gas explosion overpressure prediction of obstructed cubic chamber by Bayesian regularization artificial neuron network: Bauwens model [J]. Journal of Loss Prevention in the Process Industries, 2018, 56: 209–216. DOI: 10.1016/j.jlp.2018.05.016.