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

This paper conducted data-driven research on predicting the consequences of residential gas explosion accidents, addressing the challenge posed by the highly nonlinear nature of disaster evolution in such incidents and the difficulty in accurately predicting their outcomes. A gas explosion accident consequence prediction method based on artificial neural network was proposed. By utilizing extensive numerical simulations, a diverse gas explosion consequence dataset encompassing various residential types was created. Through sensitivity analysis and accuracy verification, an intelligent prediction model for gas explosion consequences was developed. 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.