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