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When designing complex novel buildings such as a modern airport or a mall with a large atrium, fire protection engineers have long relied on computational fluid dynamics (CFD) simulations to predict how fire and smoke will spread through the structure. Such tests are necessary for designing fire protection strategies for projects relying on performance-based design, where the parameters of the building fall outside the scope and guidance of traditional codes and standards.
The problem, experts say, is that these large CFD simulations are time-consuming, costly, and take a lot of computational time, making it difficult for a reviewer to check several different designs for optimal performance. To solve that problem, artificial intelligence researchers are developing new systems run by a trained AI program that can do the same calculations and modelling in a fraction of the time. What’s more, instead of relying on a static model in the manner of traditional simulations, the AI system is able to learn from the experiences and data generated from each new test, thereby improving its accuracy over time.
Once the models are trained, an engineer would only need to input parameters such as building dimensions, fire size, and fuel type, and will get results almost instantly.
Several AI-related projects in the works aim to use AI to make it easier to design better fire protection systems for buildings. For instance, one AI program is being developed to instantly calculate the ceiling heights and slopes in an entire building and estimate the optimal location for each sprinkler and smoke detector to maximize effectiveness in the event of a fire. Another AI program under consideration is akin to a toolbox for sprinkler design; by training it on various codes and standards, the program may soon be capable of telling a sprinkler designer where exactly in a building to place each sprinkler to achieve compliance and optimal performance.
Still another project, initiated by the Fire Protection Research Foundation, has trained AI cameras to go through a building and use visual data to identify various objects and calculate the total fuel load of the building and its contents. Such measurements are critical in performance-based designs for ensuring adequate fire protection. In one early test, the AI surveyed three office buildings and found that “the measured total fuel load densities…were considerably larger than values found in older surveys and most code provisions,” according to the project’s final report.
Using a combination of sensors and AI, some experts envision fire departments someday leading a fire response remotely using what’s called a digital twin: a to-scale and real-time digital representation of a building, tunnel, or other structure on fire.
In a fire scenario, fire commanders could create a digital twin of a fire on a computer screen to see the real-time outline of the fire’s exact location as it grows and spreads throughout a building. This might include dots representing the location of real firefighters or firefighting robots as they work to extinguish the fire. Overlayed on the screen would be anything deemed useful—the status and location of sprinklers or fire pumps, exit locations, temperature readings, air speed and direction, the movement of pedestrians, or even predictive analysis of how conditions might change from minute to minute. Fire commanders could control aspects of the response, such as smoke ventilation, by clicking a button and opening a ceiling window inside the structure. Such technology would require many more sensors, smart building technologies, and real-time data transfer, experts say, and may still be years away.
Even so, digital twins are already finding applications including manufacturing production lines, where a worker at a computer can run the line like the video game Sim City, pointing and clicking to tell the machines what to do and even changing real-world variables simply by manipulating them on the screen.
Existing novel methods for smart evacuation and crowd management could soon get a boost from faster AI programs.
Dynamic exit signs direct occupants to the optimal and safest route of egress in the event of a fire or other emergency, depending on the specific conditions inside the building. For such a system to work, a huge amount of data from sensors must be processed in real time to understand where in a building smoke and heat exist—and critically, where they are moving—and then coordinate potentially hundreds of different exit signs throughout the building to display the correct egress information. As conditions change, so might the optimal path of exit. The realization of such complex exit strategies will almost certainly depend on AI and machine learning systems to process the information and then predict how the hazards will change over time.
Crowd management tools are also expected to get a big boost from AI. Basic tools already exist that can process information from security cameras, sensors, cell phone data, and even social media to evaluate crowd count in a given area and create density maps. Layering powerful AI algorithms over these programs could help officials at major events analyze crowd movement patterns faster to proactively manage the flow of people and avoid potentially hazardous situations. This system, used in conjunction with tools including dynamic signage, may one day automatically detect bottlenecks and other hazards at large events and use signage to steer people to prevent overcrowding.
