Close this search box.

We are creating some awesome events for you. Kindly bear with us.

AI Helps Alert U.S. Firefighters of Imminent Danger

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.


Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.


CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

Well-known for our strengths in system integration and consultation, CTC Global proves to be the preferred IT outsourcing destination for organizations all over Singapore today.


Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit


SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.


HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 


IBM is a leading global hybrid cloud and AI, and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.