NIST researchers developed a new AI model that can identify safe evacuation routes during a fire. The model can be used with new electronic exit signs, called dynamic emergency exit displays, to show whether an exit is safe to use.
A. Kim/NIST
A fire alarm jolts you from your office desk, and you head for the nearest exit. But what if the closest exit has already been blocked by the fire?
Researchers at the National Institute of Standards and Technology (NIST) and their colleagues have developed a new AI model called Safe Step that can redirect occupants to the safest evacuation route in a fire. Described in the Journal of Building Engineering, the model can be used with electronic displays to show whether an exit is safe to use.
"Fires can grow and spread," said Hongqiang "Rory" Fang, a research associate at NIST and first author of the journal paper. "Our model forecasts how the fire is evolving and can help update emergency exit displays to direct people toward the safest exit."
Safe Step can be used in "smart" buildings, where sensors monitor real-time environmental conditions, such as temperature and air quality. Some of these buildings are testing a new technology called a dynamic emergency exit display, which can indicate that the exit is safe to use or point arrows to a safer route out of the building.
Previous research has proposed using traditional algorithms to find the shortest path for safely evacuating a building fire. However, these algorithms depend entirely on current building conditions and do not consider the cumulative hazards that evacuees can face along the route.
"We asked ourselves, 'Can we build a better algorithm that predicts how the fire evolves, and in a way that helps save more lives?'" said NIST mechanical engineer Wai Cheong Tam.
Machine Learning for Safe Evacuations
Their model, Safe Step, uses a type of AI known as reinforcement learning. It makes decisions on the safest routes through trial and error. Safe Step uses the building layout to learn evacuation routes and data from a NIST fire simulation tool to anticipate how a fire in the layout develops over time.
During training, the model learns to forecast how a fire will affect occupants and then guides them to safer evacuation routes. In real-world use, the model does not need to run a simulation of the fire in real time. Instead, it would rely on live sensor data from the building to continuously adjust its recommendations as the fire evolves.
The algorithm needs numbers, though, to determine whether it's choosing the best route. So NIST researchers used a fire safety metric called the fractional effective dose (FED) of toxic gases. This variable represents the severity of fire hazards to which a person is exposed over time. The lower the FED, the lower the hazard exposure for the occupants. The model chooses the route with the lowest FED, accounting for how toxic gas exposure changes over time as an occupant moves.
Researchers then used the model in two test cases to compare with the traditional algorithm. They also used a more complex single-level building structure and found that the model consistently gave safe evacuation routes.
For example, suppose a fire starts in a room across the hallway, and a small amount of smoke spreads into the hallway. A traditional algorithm would guide the occupant to cross the hallway to get to the closest exit. But what happens if the fire continues to grow and becomes extremely dangerous by the time the occupant crosses the hallway and approaches the exit? That nearest exit is no longer a safe option. Safe Step can anticipate this change and provide data for dynamic exit signs to direct the occupant to a more distant but safer exit at the opposite end of the hallway.
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