Machine Learning Method for Predicting Smoke Blockage Time at Apartment Evacuation Exits

Abstract

The article explores the application of machine learning methods to study the time of smoke blockage of evacuation routes during the initial stage of a fire in residential premises. A dataset was formed through numerical experiments conducted in the PyroSim software, where 140 fire scenarios were modeled with varying values of the fire spread angle, distance to the exit, total area of opened doors and windows. Correlation analysis was performed to assess relationships between parameters, and polynomial regression of the second degree with variable scaling was employed for modeling, yielding interpretable coefficients. The results were validated using mean squared error (MSE) and coefficient of determination (R²), complemented by visualizations of dependencies. The study demonstrates the effectiveness of combining numerical modeling with machine learning for predicting smoke blockage time, offering practical implications for enhancing evacuation safety