Predicting temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings by artificial neural networks

Abstract

Predicting temporal evacuation travel time in staircases between adjacent floors is crucial for dynamic evacuation guidance to improve vertical evacuation efficiency during emergencies in super high-rise buildings. However, the prediction methods used in previous studies mainly focus on the total evacuation time in buildings and cannot predict the temporal evacuation travel time in every single part of the total evacuation route. This study proposes a novel method to predict temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings by using artificial neural networks. Firstly, evacuation simulation data of a 71-storey office building are analyzed for feature engineering and dataset building. Secondly, three types of artificial neural networks, including convolutional neural networks, RNN-based model (the traditional recurrent neural networks, long-short term memory networks, and bidirectional long-short term memory networks), and Attention-based model (Attention model and Transformer model) are extended to predict the temporal evacuation travel time in staircases between adjacent floors based on the simulation data. Finally, the prediction results by different artificial neural networks are compared. The results show that the artificial neural networks can provide accurate prediction values for the temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings.