Evaluating Smart Building Features for Fire, Electrical, and Life Safety: A Rapid Human–LLM Framework for Literature Review and Research Mapping

Abstract

The rapid integration of smart technologies into modern buildings is fundamentally transforming fire, electrical, and life safety (FELS) systems. This paper introduces a hybrid Human–Large Language Model (LLM) framework designed to efficiently conduct large-scale literature reviews, systematically map existing research, and identify critical knowledge gaps in smart building safety. Leveraging advanced LLMs for high-throughput summarization, topic modeling, and gap analysis, combined with expert validation, this method ensures both scalability and domain-specific rigor. The study analyzes 1,409 publications retrieved from Scopus, culminating in a refined corpus of 83 high-quality articles categorized into nine thematic clusters, including advanced sensing technologies, automation, enhanced connectivity, digital twins, cybersecurity, standard compliance, sustainability, specialized applications, and decision-making in disaster response. Detailed gap analyses reveal significant challenges related to real-world validation of AI-based systems, interoperability among IoT devices, cybersecurity vulnerabilities, and the need for dynamic evacuation and hazard modeling. The resulting knowledge map and research roadmap provide actionable insights for researchers, practitioners, and policymakers aiming to advance safer, smarter, and more resilient built environments. The proposed framework demonstrates how AI-assisted methodologies can accelerate knowledge synthesis while preserving analytical depth, offering a scalable solution for rapidly evolving interdisciplinary research domains.