A multiple back propagation neural network fusion algorithm for ceiling temperature prediction in tunnel fires

This study presents a multiple back propagation neural network (BPNN) fusion algorithm to predict the ceiling temperature distribution and the maximum ceiling temperature in tunnel fires for better firefighting decisionmaking. The algorithm belongs to a kind of data-driven artificial intelligence methods, which is not limited to the special tunnel fire scenes. To improve the prediction precision, a strong multiple BPNN including some subBPNNs and an additional external iteration procedure are used in the algorithm. Large-scale pool fire experiments under different ventilation conditions were carried out to investigate the ceiling temperature character of tunnel fires and verify the algorithm. The results show that the algorithm can predict well the ceiling temperature distribution and the maximum ceiling temperature under different ventilation conditions, which has a higher prediction precision than the traditional single BPNN algorithm and some developed theoretical models.

Automated summary

This study presents a multiple back propagation neural network (BPNN) fusion algorithm to predict the ceiling temperature distribution and the maximum ceiling temperature in tunnel fires. The algorithm is a data-driven artificial intelligence method that is not limited to specific tunnel fire scenes. It utilizes a strong multiple BPNN and an additional external iteration procedure to improve the prediction precision. Large-scale pool fire experiments were conducted to verify the algorithm and the results show that it outperforms traditional BPNN algorithm and developed theoretical models in predicting ceiling temperature in different ventilation conditions.