An Artificial Neural-network Based Predictive Model for Pre-evacuation Human Response in Domestic Building Fire

The post-1993 WTC attack study (Proulx and Fahy, In: Proceedings of ASIAFLAM’95—An International Conference on Fire Science and Engineering, Hong Kong, 1995, pp 199–210) revealed that occupants took 1–3 h to leave the 110-storey buildings, and the pre-movement reactions could account for over two-thirds of the overall evacuation time. This indicates that a thorough understanding of the pre-evacuation behavioral response of people under fire situations is of prime importance to fire safety design in buildings, especially for complex and ultra high-rise buildings. In view of the stochastic (the positions of the occupants) and fuzzy (uncertainty) nature of human behavior (Fraser-Mitchell, Fire Mater 23:349–355, 1999), conventional linear and polynomial predictive methods may not satisfactorily predict the people’s response. An alternative approach, Adaptive Network based Fuzzy Inference System (ANFIS), is proposed to predict the pre-evacuation behavior of peoples, which is an artificial neural network (ANN) based predictive model and integrates fuzzy logic (if-then rules) and neural network (based on back propagation learning procedures The ANFIS learning architecture can be trained by structured human behavioral data, and different fuzzy human decision rules. The applicability in simulating human behavior in fire is worth exploring.