Incidents involving the leakage of flammable and explosive chemicals in highway tunnels can lead to disastrous events such as fires and explosions. The distinctive features of highway tunnels, characterized by their confined and elongated structure, intensify the complexity of managing such accidents, potentially resulting in significant casualties and property damage. Rapid detection and acquisition of leakage source information during a flammable gas leak in a highway tunnel are of utmost importance for effective accident management. This study introduces a predictive model developed using a machine learning approach. The model uses time series data from gas concentration sensors and anemometers installed within the tunnel to predict parameters such as gas leakage velocity, source location, and initiation time. A computational fluid dynamics simulation of gas leakage within the tunnel was constructed, and a dataset was generated using sample generation and preprocessing methods. The results demonstrate that this method exhibits robust predictive performance. Furthermore, a prediction strategy is proposed to enhance the model’s predictive accuracy and resilience to data noise. This model offers valuable insights for improving accident management strategies for gas-related incidents in highway tunnels.
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