In the heart of Chongqing, a city known for its dense urban landscape and rapid industrial growth, researchers are tackling a pressing issue: predicting fire evolution in complex building environments. Hui Xu, a researcher at Chongqing University of Posts and Telecommunications, has led a groundbreaking study that combines knowledge graphs and machine learning to revolutionize fire risk management. Published in the *Journal of Asian Architecture and Building Engineering* (translated as *Journal of Asian Architecture and Building Engineering*), this research offers a data-driven approach to enhance public safety and mitigate risks in high-rise clusters and interconnected infrastructure.
The study, which analyzed 510 fire accidents in Chinese building engineering from 2000 to 2024, constructs a comprehensive knowledge graph that integrates diverse urban data. This graph serves as a foundation for three predictive models: logical convolutional neural network (L-CNN), random forest, and k-nearest neighbors. Among these, the L-CNN model stood out, achieving an impressive accuracy of 83.29% and demonstrating superior adaptability in complex fire scenarios.
“Traditional methods often rely on subjective and static approaches, which are inefficient and lack scalability,” explains Hui Xu. “By integrating knowledge graphs with machine learning, we can improve prediction accuracy and practical applicability, supporting early warning systems and emergency decision-making.”
The implications of this research extend beyond fire safety. In the energy sector, where high-rise buildings and interconnected infrastructure are common, accurate fire evolution prediction can significantly reduce risks and enhance safety protocols. The ability to predict fire spread with high accuracy can inform better building design, emergency response planning, and risk management strategies.
Moreover, the study’s sensitivity analysis rigorously assesses the robustness of the predictive models, ensuring that the outputs are reliable and actionable. This level of detail is crucial for stakeholders in the energy sector, where decisions must be made quickly and accurately to protect both assets and lives.
As urbanization continues to accelerate, the need for advanced fire risk management becomes increasingly critical. Hui Xu’s research provides a blueprint for how data-driven approaches can transform fire safety management, offering a scalable and adaptable solution for complex urban environments.
In the words of Hui Xu, “This research supports early warning systems and emergency decision-making, advancing data-driven fire safety management.” The integration of knowledge graphs and machine learning not only enhances prediction accuracy but also paves the way for more effective risk mitigation strategies in the future.
As the energy sector continues to evolve, the insights gained from this research could shape future developments in building engineering and fire safety, ensuring that our urban landscapes are safer and more resilient.