Study on real-time heat release rate inversion for dynamic reconstruction and visualization of tunnel fire scenarios

This study proposes a framework integrating tunnel fire knowledge with the machine learning approach to provide key fire parameters and reconstruct tunnel fire scenarios. The machine learning approach establishes mapping rules between tunnel ceiling temperatures measured by Fiber Bragg Grating (FBG) system and the realtime heat release rate (HRR) based on the tunnel fire database. The database employed in training and testing algorithms was generated by numerical simulations and full-scale tunnel fire tests, respectively. In addition, the HRR database establishment procedures, hyperparameter selection of machine learning algorithms and test dataset selection are systematically analysed. The real-time HRR inversion performance of four machine learning methods (support vector regression (SVR), random forests (RF), multilayer perceptron (MLP), long short-term memory network (LSTM)) and two tunnel fire knowledge methods (maximum ceiling gas temperature, smoke back-layering length) are evaluated. It is shown that the LSTM method provides the best prediction accuracy on full-scale tunnel fire test datasets, and the RF method is found to be most resilient despite the loss of partial temperature data due to the failure of temperature sensors. The corresponding models have been verified by the full-scale tunnel fire tests, and can provide the scientific decision support for intelligent fire-fighting.

Automated summary

This study proposes a framework that combines tunnel fire knowledge with machine learning to provide key fire parameters and reconstruct tunnel fire scenarios. By establishing mapping rules between tunnel ceiling temperatures and real-time heat release rate using a Fiber Bragg Grating (FBG) system, the machine learning approach shows promising performance in predicting fire situations.