A Review of Building Evacuation Models - NIST Virtual Library

A.18 CRISP3
Developer: J. Fraser-Mitchell, BRE, UK
The stand-alone evacuation model is the focus of this write-up.
Purpose of the model: The purpose of this model is to simulate entire fire scenarios
incorporating a Monte Carlo technique 11, 78-81 . There is also an option to simulate an evacuation
using the external or “stand-alone” evacuation model, which does not incorporate the zone fire
model effects or the toxicity effects to the occupants. In this mode, the model will run in fire
drill mode, but the Monte Carlo technique can still be used.
Availability to the public for use: CRISP is used only by BRE for in-house consultancy.
Modeling method: This is a behavioral model.
Structure of model: This is a fine network system. The model uses a 0.5 m by 0.5 m grid over
the entire floor plan that is used to move occupants around the building. This grid size can be
larger, but the developers warn that the larger the grid size, the lower the accuracy of the
evacuation results. The occupants follow a contour map that is spread throughout the floor plan.
Perspective of model and occupant: The model views the occupants as individuals by giving
the occupants certain behavioral roles, and in turn, certain behavioral activities that will take
place during the evacuation, in a probabilistic fashion. The user also specifies the occupant’s
walking speed and height (distributions), as well as probabilities for being asleep and located in
certain places throughout the building.
The occupants’ view of the building is also individual because although the model defaults to
move the occupants to the nearest exit, the user can alter the shortest route by indicating a high
“door difficulty” for a certain exit. Also, door difficulties change and increase with the presence
of smoke.
Occupant behavior: Rule-based or conditional behavior. The population is assigned
occupational and role data, on the basis of probabilities. The occupation data determines the
location probabilities, sleeping probabilities, head height, and movement speed of each group.
The role data dictates actions (behaviors of the group) and associated probabilities of each
behavior. Behavior is performed in the model in the form of actions, which are each associated
with a delay time, degree of difficulty, and urgency level. Actions do not have to continue until
they are complete, but may be interrupted by conditions within the model. In this case, another
action will take place. Some example actions to choose from in the model are search rooms,
rescue, investigate, escape, complete work, trapped, unconscious, asleep, etc. An example of
simulated behavior of a fire fighter is explained here.
“Depending upon the conditions – the fire fighters will start off ‘safe’ which will
prompt them to investigate (which has a 100 % chance of occurring). This will
lead them to go begin traveling to the room of origin. Under the investigation
action, there are three different conditions that will prompt another action (and
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