Study on Maximum Temperature Under Multi-Factor Influence of Tunnel Fire Based on Machine Learning

This study proposes a machine learning framework utilizing physical feature dimensionality reduction to address the problem of predicting the maximum excess temperature beneath the tunnel ceiling under the influence of multiple factors. First, theoretical analysis is used to systematically explore the impacts of various factors on the maximum excess temperature, including the heat release rate of the fire source, tunnel height, slope, and ambient air pressure. Physical relationships are established to identify key factors, remove redundant features, and construct a simplified feature vector set. Five typical machine learning models are selected: Random Forest (RF), Support Vector Regression (SVR), Fully Connected Neural Network (FCNN), Multi-Layer Perceptron (MLP), and Bayesian Neural Network (BNN). A hybrid data collection strategy combining scale model tests and CFD numerical simulations constructs a small-sample structured dataset with physical backgrounds. The models are evaluated regarding prediction accuracy, stability, and generalization ability. Results show that the Bayesian Neural Network (BNN) optimized by random search parameter optimization and Bayesian regularization significantly outperforms other comparative models in evaluation indices such as root mean square error (RMSE), and mean absolute error (MAE), and coefficient of determination (R2), making it the optimal model and algorithm combination for such tasks. This study provides a reliable quantitative analysis method for tunnel fire safety assessment and offers a new methodological reference for the research on fire dynamics in underground spaces.