Full-field temperature prediction in tunnel fires using limited monitored ceiling flow temperature data with transformer-based deep learning models

In practical tunnel scenarios, full-field coverage of sensors is impractical and costly. During a tunnel fire, the available information is constrained and localized, making the prediction of full-field smoke temperature distribution becoming a noteworthy challenge. This study proposes a transformer-based deep learning model to predict full-field smoke temperature distributions during fire incidents in real-time using limited temporal data from the sensors installed in localized regions below the ceiling, considering heat release rate of the fire source is unknown. The results indicate that proposed approach can predict the longitudinal temperature distribution throughout the tunnel with a length of 750 m by leveraging temperature data from limited sensors within a monitoring length of 210 m. It can further predict the vertical temperature profiles, and eventually estimate the full-field temperature distribution within the tunnel. The transformer model achieved R² of 0.95 and 0.87 for longitudinal and vertical temperature distribution predictions, respectively. Under the influence of the self-attention mechanism, the transformer model has an advantage over the long short-term memory model in capturing global information, enhancing the accuracy of longitudinal temperature distribution predictions by 18.8 %. This study significantly contributes to effective emergency response and rescue strategies during tunnel fire incidents.