Early design stage evaluation of architectural factors in fire emergency evacuation of the buildings using Pix2Pix and explainable XGBoost model

As a critical factor in architectural design, emergency evacuation is influenced by numerous parameters. Designers utilize modelling software to evaluate their sketches after completion ofbasic design. However, no various alternatives of early design stages could be assessed via simulations, since it is a time-consuming procedure. In this study, deep-learning algorithms have been adapted for the assessment of the evacuation process at early design stages. The main methods applied include an image-to-image translation with a conditional GAN (Pix2Pix) and Extreme Gradient Boosting (XGBoost). The developed Pix2Pix model generates the heat maps of possible route congestions with a Structural Similarity Index (SSIM) of 0.89. Besides, the XGBoost model predicts the evacuation time with the mean absolute error (MAE) and R2 values of 36 s and 0.94, respectively. This method generates the results of intended analyses at high speed and is a reliable alternative for time-consuming evacuation simulations in early design stages.