Development and Application of an Intelligent Approach to Reconstruct the Location of Fire Sources from Soot Patterns Deposited on Walls

Abstract

This study developed an objective approach for determining fire source location based on an artificial neural network (ANN) model. The samples for the ANN model were obtained from computational fluid dynamics simulations. A data preprocessor was devised to transform numerical simulation results into a format that could be used by the ANN model prior to network training, and bootstrap aggregation was used to improve the model’s predictive performance, which was evaluated by the leave-one-out approach. The results show that the 95% left-tailed confidence limit was 0.7921 m for planar dimensions of 5 m × 5 m, which is sufficiently accurate for practical application. Additionally, comprehensive experiments were conducted in the confined space of a fire compartment that was geometrically similar to various fire source locations to explore soot patterns and verify the ANN model. The experimental results reveal that the differences between the locations determined in scaling experiments and the locations predicted by the ANN were invariably less than 1 m. In particular, the difference was only 0.17 m when the fire source was located in the centre of the fire compartment. These results demonstrate the feasibility of the devised ANN model for reconstructing fire source location in engineering applications.