Considering building fires can turn from bad to deadly in an instant, time is essential for firefighting. However, the warning signs of danger are frequently difficult for firefighters to detect amid the mayhem. The fire service does not have many technologies that predict flashover at the scene. Firefighters often only rely on observation which can be deceiving. Seeking to remove this major blind spot, researchers at the National Institute of Standards and Technology (NIST) have developed P-Flash, or the Prediction Model for Flashover.
The AI-the powered tool was designed to predict and warn of a deadly phenomenon in burning buildings known as flashover, when flammable materials in a room ignite almost simultaneously, producing a blaze only limited in size by available oxygen. The tool’s predictions are based on temperature data from a building’s heat detectors, and. The AI is designed to operate even after heat detectors begin to fail.
The team tested P-Flash’s ability to predict imminent flashovers in over a thousand simulated fires and more than a dozen real-world fires. Research, just published in the Proceedings of the AAAI Conference on Artificial Intelligence, suggests the model shows promise in anticipating simulated flashovers and shows how real-world data helped the researchers identify an unmodeled physical phenomenon that if addressed could improve the tool’s forecasting in actual fires. With further development, P-Flash could enhance the ability of firefighters to hone their real-time tactics, helping them save building occupants as well as themselves.
Computer models that predict flashover based on temperature are not entirely new, but until now, they have relied on constant streams of temperature data, which are obtainable in a lab but not guaranteed during a real fire. Machine-learning algorithms uncover patterns in large datasets and build models based on their findings. These models can be useful for predicting certain outcomes, such as how much time will pass before a room is engulfed in flames.
To build P-Flash, the researchers fed their algorithm temperature data from heat detectors in a burning three-bedroom, one-story ranch-style home. This building was of a digital rather than brick-and-mortar variety. As machine learning algorithms require great quantities of data and conducting hundreds of large-scale fire tests was not feasible, the team burned this virtual building repeatedly using NIST’s Consolidated Model of Fire and Smoke Transport (CFAST), a fire modelling program validated by real fire experiments. The researchers ran 5,041 simulations, with slight but critical variations between each.
The findings show that the model correctly predicted flashovers one minute beforehand for about 86% of the simulated fires. Another important aspect of P-Flash’s performance was that even when it missed the mark, it mostly did so by producing false positives, predictions that an event would happen earlier than it actually did, which is better than the alternative of giving firefighters a false sense of security.
To crosscheck the findings of the data, the researchers came across an opportunity to find answers in real-world data produced by Underwriters Laboratories (UL) in a recent study funded by the National Institute of Justice. UL had carried out 13 experiments in a ranch-style home matching the one P-Flash was trained on, and as with the simulations, ignition sources and ventilation varied between each fire.
The NIST team trained P-Flash on thousands of simulations as before, but this time they swapped in temperature data from the UL experiments as the final test. And this time, the predictions played out a bit differently. P-Flash, attempting to predict flashovers up to 30 seconds beforehand, performed well when fires started in open areas such as the kitchen or living room. But when fires started in a bedroom, behind closed doors, the model could not accurately tell when flashover was imminent. The team identified a phenomenon called the enclosure effect as a possible explanation for the sharp drop-off in accuracy.
Despite revealing a weak spot in the tool, the team finds the results to be encouraging and a step in the right direction. The researchers’ next task is to zero in on the enclosure effect and represent it in simulations. To do that they plan on performing more full-scale experiments themselves. When its weak spots are patched and its predictions sharpened, the researchers envision that their system could be embedded in hand-held devices able to communicate with detectors in a building through the cloud.
Firefighters would not only be able to tell their colleagues when it’s time to escape, but they would be able to know danger spots in the building before they arrive and adjust their tactics to maximise their chances of saving lives.
As reported by OpenGov Asia, U.S. researchers have been utilising AI for mitigating disasters by inventing the Building Recognition using AI at Large-Scale (BRAILS) suite of tools. BRAILS is an AI-enabled software to assist regional-scale simulations that extracts information from satellite and street view images for being used in computational modelling and risk assessment of the built environment.
