A view from NIST's Burn Observation Bubble (BOB) of a burning structure during an experiment, one minute before flashover.
In firefighting, the worst flames are the ones you don’t see coming. Amid the chaos of a burning building, it is difficult to notice the signs of impending flashover — a deadly fire phenomenon wherein nearly all combustible items in a room ignite suddenly. Flashover is one of the leading causes of firefighter deaths, but new research suggests that artificial intelligence (AI) could provide first responders with a much-needed heads-up.
Researchers at the National Institute of Standards and Technology (NIST), the Hong Kong Polytechnic University and other institutions have developed a Flashover Prediction Neural Network (FlashNet) model to forecast the lethal events precious seconds before they erupt. In a new study published in Engineering Applications of Artificial Intelligence, FlashNet boasted an accuracy of up to 92.1% across more than a dozen common residential floorplans in the U.S. and came out on top when going head-to-head with other AI-based flashover predicting programs.
Flashovers tend to suddenly flare up at approximately 600 degrees Celsius (1,100 degrees Fahrenheit) and can then cause temperatures to shoot up further. To anticipate these events, existing research tools either rely on constant streams of temperature data from burning buildings or use machine learning to fill in the missing data in the likely event that heat detectors succumb to high temperatures.
Until now, most machine learning-based prediction tools, including one the authors previously developed, have been trained to oper ..
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