@article{HU2026, 
author = {Qianran HU and Xingyu SHEN and Qi ZHANG and Mengqi YUAN and Wulong FAN and Jizhe WANG and Huijie YANG and Rui LIN},
title = {Prediction of gas explosion consequences in residential buildings based on artificial neural network},
year = {2026},
journal = {Explosion and Shock Waves},
volume = {46},
number = {5},
keywords = {residential buildings, gas explosion, artificial neural network, consequence prediction, explosion overpressure},
url = {https://www.sciopen.com/article/10.11883/bzycj-2025-0382},
doi = {10.11883/bzycj-2025-0382},
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.}
}