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

Simple item page
dc.contributor.authorLeiva-Araos, Andrés
dc.contributor.authorKalasapudi, Vamsi
dc.contributor.authorJiang, Aiyin
dc.contributor.authorKaushal, Hemani
dc.date.accessioned2026-10-06T20:56:57Z
dc.date.available2026-10-06T20:56:57Z
dc.date.issued2025
dc.description.abstractThe 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 realworld 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.
dc.description.versionVersión Publicada
dc.identifier.citationLeiva-Araos, A., Kalasapudi, V. S., Jiang, A., & Kaushal, H. (2025). Evaluating Smart Building Features for Fire, Electrical, and Life Safety: A Rapid Human-LLM Framework for Literature Review and Research Mapping. IEEE Access, 13, 173312-173333. https://doi.org/10.1109/ACCESS.2025.3613246
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2025.3613246
dc.identifier.urihttps://hdl.handle.net/11447/11239
dc.language.isoen
dc.subjectSmart buildings
dc.subjectFire safety
dc.subjectDisaster response systems
dc.subjectKnowledge map
dc.subjectLarge language models (LLM)
dc.subjectResearch gap analysis
dc.subjectAI-assisted literature review
dc.titleEvaluating Smart Building Features for Fire, Electrical, and Life Safety: A Rapid Human-LLM Framework for Literature Review and Research Mapping
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceIEEE Access
dspace.entity.typePublication
relation.isAuthorOfPublication180f71c7-c05d-46b4-9d74-aaf047e2f270
relation.isAuthorOfPublication.latestForDiscovery180f71c7-c05d-46b4-9d74-aaf047e2f270

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Evaluating_Smart_Building_Features_for_Fire_Electrical_and_Life_Safety_A_Rapid_Human-LLM_Framework_for_Literature_Review_and_Research_Mappin.pdf
Size:
7.58 MB
Format:
Adobe Portable Document Format
Description:
Texto Completo
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
347 B
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Artículos Ingeniería

Implementado por OpenGeek Services