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

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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. 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. 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. doi: 10.11883/bzycj-2025-0382

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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, 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

Abstract

Abstract

A data-driven study was conducted to tackle the highly nonlinear and uncertain evolution of residential gas explosion disasters and to achieve accurate prediction of their consequences. The primary objective was to develop an efficient and intelligent predictive tool for key explosion parameters—maximum overpressure, maximum temperature, and their spatial locations—across diverse residential layouts. Therefore, A gas explosion accident consequence prediction method based on artificial neural network was proposed. Firstly, computational fluid dynamics technology was employed to establish numerical models of three typical residential types. Secondly, full-scale gas explosion experiments were conducted to validate the accuracy of the numerical simulations, alongside extensive computational analyses, yielding a diverse dataset of gas explosion consequences spanning various residential types. Finally, through sensitivity analysis and accuracy verification, an intelligent model was developed to accurately predict the consequences of gas explosions. The model demonstrated prediction errors of less than 15% for indoor maximum explosion overpressure, less than 5% for temperature, and spatial position coordinated errors of less than 25%. In this way, the batch prediction of the most severe indoor explosion consequences and their spatial location characteristics for various residential types under any ignition position was realized. The results show that as the house area expands and spatial layout complexity increases, the maximum overpressure and temperature values also rise accordingly. The living room consistently exhibits the lowest overpressure levels, while areas near bedroom walls lacking vent tend to experience extreme overpressure and temperature values. Ignition in the kitchen and bedroom can result in the most severe overpressure and temperature consequences in the respective rooms, showcasing the varying impact of ignition position on explosion outcomes. The research conclusions provide an important reference for further expanding the prediction application of artificial intelligence in the field of gas explosion and the efficient prevention and control of explosion accidents.
- residential buildings,
- gas explosion,
- artificial neural network,
- consequence prediction,
- explosion overpressure

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References(32)

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