In the rapidly evolving landscape of smart buildings, a groundbreaking study published in the IEEE Access journal is set to redefine how we approach fire, electrical, and life safety (FELS) systems. Led by Andres Leiva-Araos from the School of Computing at the University of North Florida, this research introduces a novel framework that combines human expertise with the power of large language models (LLMs) to conduct comprehensive literature reviews and identify critical knowledge gaps in smart building safety.
The study, which analyzed 1,409 publications, ultimately refined a corpus of 83 high-quality articles into nine thematic clusters. These clusters range from advanced sensing technologies and automation to digital twins, cybersecurity, and sustainability. The research highlights significant challenges, such as the real-world validation of AI-based systems, interoperability among IoT devices, and cybersecurity vulnerabilities.
Leiva-Araos explains, “Our framework leverages advanced LLMs for high-throughput summarization, topic modeling, and gap analysis, combined with expert validation. This ensures both scalability and domain-specific rigor.” This hybrid approach not only accelerates the research process but also provides actionable insights for researchers, practitioners, and policymakers.
The implications for the energy sector are profound. As buildings become smarter, the integration of advanced sensing technologies and automation can lead to more efficient energy management systems. This can result in significant cost savings and reduced environmental impact. Additionally, the study’s focus on cybersecurity and interoperability addresses critical concerns for energy providers, ensuring that smart buildings remain secure and reliable.
One of the most compelling aspects of this research is its potential to shape future developments in the field. By identifying key knowledge gaps, the study provides a roadmap for future research and innovation. For instance, the need for dynamic evacuation and hazard modeling could lead to the development of more sophisticated disaster response systems, ultimately saving lives and minimizing damage.
Leiva-Araos adds, “The resulting knowledge map and research roadmap offer actionable insights for advancing safer, smarter, and more resilient built environments.” This research not only accelerates knowledge synthesis but also preserves analytical depth, offering a scalable solution for rapidly evolving interdisciplinary research domains.
As the energy sector continues to embrace smart technologies, this study serves as a crucial guide for navigating the complexities of FELS systems. By addressing critical challenges and providing a clear path forward, it paves the way for a future where smart buildings are not only efficient and sustainable but also safe and secure.
Published in the IEEE Access journal, known in English as “Access to IEEE,” this research marks a significant step forward in the integration of AI-assisted methodologies in the construction and energy sectors. The study’s findings are set to influence policy, practice, and future research, making it a must-read for professionals in the field.