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

Abstract

that occupants took 1-3 h to leave the 110-storey buildings, and the pre-movement reactions could account for over twothirds 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 highrise 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 peopleÕs 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.

Intelligent Evacuation Management Systems

“…An Adaptive Network based Fuzzy Inference System (ANFIS) has been proposed by Lo et al [2009] to predict the pre-evacuation behaviour of evacuees based on back propagation oriented learning procedures. The authors have modelled their proposed soft computing approach by training the network with data acquired from structured human behaviour.…”

Rough Set Approach for Group Evacuation Behavior Analysis in Passenger Transport Hub Area

“…Over the last few decades, considerable research has focused on evacuation behavior analysis related to hurricane evacuation [1][2][3][4][5][6] and building fire evacuation [7,8]. The majority of existing studies focused on individual and household evacuation decision-making and behavioral responses.…”

“…By far, several methods have been put forward for helping us understand evacuation decision-making, including contingency table analysis [8], artificial neural network [7], and logistic regression analysis [4,6,9,10]. Among the above methods, contingency table analysis is only used to determine whether dependence exists between evacuation decision-making and related factors for a given significant level.…”

Modeling loss and no-loss fire incidents using artificial neural network: Case of Toronto

“…His exemplified case study showed that the reliable evaluation result could be obtained in shorter time as long as the values of related fire risk evaluation indexes are input into the model. Lo et al [6] proposed an Adaptive Network Fuzzy Inference System based algorithm to predict the pre-movement reaction of people in fire. Their training results showed that such an approach can be applied in manipulating the limited data of human reaction in fire and can serve as part of an evacuation model to predict the initial response of the evacuees.…”