Fire, especially in skyscrapers, highlights the urgent need for smarter, faster and more reliable firefighting solutions tailored to these high-tech spaces. In smart buildings, where every second counts and the stakes are higher, conventional fire detection and response methods are no longer adequate.
Prof. Asif Sohail USMANI, Chair Professor in Building Sciences and Fire Safety Engineering of the Department of Building Environment and Energy Engineering at The Hong Kong Polytechnic University, and his research team address these challenges by introducing a novel approach that leverages Artificial Intelligence of Things (AIoT) and Digital Twin technologies for super real-time fire forecasting and management.
AIoT is the integration of AI and the Internet of Things (IoT). IoT is a network collecting data from sensors and transmitting the data through the internet. It is widely used in fire detection systems to collect real-time data such as smoke, heat and gas, and to optimise emergency response. With AI embedded into IoT, AIoT can analyse and learn the data for more accurate forecasting. Digital Twin is a virtual representation (a virtual twin) of physical objects or systems. It uses real-time data to accurately reflect the real-world situation.
The AIoT-integrated Digital Twin system is designed to bridge the gap between fragmented sensor data and comprehensive situational awareness, enabling decision-makers to anticipate fire dynamics and coordinate effective responses in complex building environments. The research titled, “AIoT-powered building digital twin for smart firefighting and super real-time fire forecast,” was published in Advanced Engineering Informatics.
This integration of IoT hardware and AI enables rapid detection of fire hazards, supports automated safety responses and provides the foundation for super real-time forecasting of impending critical events and decision-making within the Digital Twin environment.
The AutoDecoder Long Short-term Memory Neural Network (ADLSTM-Fire), a hybrid deep learning model the team developed, processes these sensor data to reconstruct high-dimensional temperature fields and forecast future developments up to 60 seconds in advance. By combining AutoDecoder and Long Short-Term Memory (LSTM) neural networks, the model transforms sparse sensor inputs into detailed spatiotemporal maps of fire progression. This predictive capability is essential for smart buildings, where early warnings and dynamic risk assessment can prevent escalation and guide evacuation strategies.
Information interaction within the system is managed through a multi-layered architecture, encompassing physical sensing, virtual data processing and user application interfaces. Sensor data are transmitted to a local router and uploaded to a cloud server, where the ADLSTM-Fire model operates within a Digital Twin platform. This platform, integrated with Building Information Modelling, offers a user-friendly interface for visualising temperature distributions, identifying hazardous regions and issuing commands for physical interventions. The modular design ensures seamless communication between hardware, software and users, supporting both real-time monitoring and strategic decision-making.
Test results from numerical simulations and real-world experiments demonstrate the system's accuracy and robustness. The real-time reconstruction model achieved an accuracy of 93%, while the ADLSTM-Fire advance forecast model reached 92%. Both models predicted the spatial and temporal evolution of temperature fields with inference times under 0.5 seconds, delivering super real-time insights into fire dynamics.
The integration of AIoT and Digital Twin technology marks a significant advancement in fire safety for smart buildings. The demonstrated accuracy, speed and adaptability of the ADLSTM-Fire model highlight its potential to enhance urban resilience, reduce fire casualties and support the development of safer, smarter cities. As research continues to refine these models and expand their applicability, AIoT-driven fire safety systems are poised to become an essential component of future urban infrastructure.
Prof. Usmani has, for 30 years, primarily worked in the field of fire safety engineering and structural fire engineering. In 2020, his proposed project "SureFire: Smart Urban Resilience and Firefighting" was awarded HK$ 33.33 million from the Hong Kong Research Grants Council Theme-based Research Scheme. As an extension of FireGrid, SureFire is developed typically for large building compartments like the skyscrapers commonly seen in Hong Kong.
The AIoT-integrated Digital Twin system in this study is part of the SureFire system. The team's work was awarded the 2026 Philip Thomas Medal of Excellence for the best paper presented at IAFSS 2023, which was titled "Introducing an active opening strategy to mitigate large open-plan compartment fire development."
Source: Innovation Digest 7
