A delay in the pre-evacuation reaction may be one of the reasons causing occupants to be ÔtrappedÕ in a dangerous zone. Under fire situations, people are found to behave differently in that some may start evacuation immediately, some may ignore the fire alarms and engage in their activities and some others may participate in fighting the fire. These behavioral reaction patterns are influenced by some factors, such as occupant characteristics, building characteristics and fire characteristics. The purpose of this study is to investigate the pre-evacuation behavior of occupants under fire and explore the associations between these factors and the human behavior. To obtain the human behavioral information in real fire, a post-fire survey for a multi-storey office building fire in a major city in Mainland China was carried out with the assistance of local fire professionals. Some of the possible factors that might influence the occupantsÕ actions at recognitions and response stage were examined. It was reckoned that the behavioral reaction at recognition and response stage was mainly dependent on the human characteristics and building characteristics except the fire characteristics. The results also implied that pre-evacuation time was typically influenced by the occupant characteristics.

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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.

Fire risk ranking is particularly useful for building designers to compare two different solutions to assess if the safety is similar. However, the multi-criteria and imprecise nature of the fire safety attributes in buildings has caused difficulties in quantifying the fire safety level. Further to the previous works, the author presented a fuzzy synthetic evaluation system for computing the fire risk ranking of buildings. It could serve for multi-level fire safety assessment framework. Linguistic terms were adopted instead of subjective numerical values. A twolevel evaluation model, including fuzzy optimal classification model and linear weighted mean model, was developed to facilitate the synthetic process. A case of fire safety ranking in high-rise residential buildings in Hong Kong was presented for illustration purpose. The evaluation result was analyzed by the maximum membership degree principle method.

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