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            Chapter Transforming Building Industry Knowledge Management: A Study on the Role of Large Language Models in Fire Safety Planning

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            Auteur
            Ashkenazi, Ori
            Isaac, Shabtai
            Giretti, Alberto
            Carbonari, Alessandro
            Durmus, Dilan
            Language
            English
            Afficher la notice complète
            Résumé
            This paper discusses the potential use of AI in general, and large language models (LLMs) in particular, to support knowledge management (KM) in the building industry. The application of conventional methods and tools for KM in the building industry is currently limited due to the large variability of buildings, and the industry’s fragmentation. Instead, relatively labor-intensive methods need to be employed to curate the knowledge gained in previous projects and make it accessible for use in future projects. The recent development of LLMs has the potential to develop new approaches to KM in the building industry. These may include querying a variety of relatively unstructured documents from previous projects and other textual sources of technical expertise, processing these data to create knowledge, identifying patterns, and storing knowledge for future use. A proposed framework is defined for the use of LLMs for KM in construction. We will perform preliminary analyses on how to train models that can generate information and knowledge required to make decisions in the development of specific tasks of fire safety planning
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/174950
            Keywords
            Large Language Models (LLMs); Knowledge Management (KM); Fire Safety Planning; Expert Systems (ESs); Artificial Intelligence (AI); Knowledge Graph; Ontology; thema EDItEUR::C Language and Linguistics::CF Linguistics; thema EDItEUR::D Biography, Literature and Literary studies::DS Literature: history and criticism
            DOI
            10.36253/979-12-215-0289-3.73
            ISBN
            9791221502893
            Publisher
            Firenze University Press
            Publisher website
            www.fupress.com/
            Publication date and place
            Florence, 2023
            Series
            Proceedings e report,
            Pages
            10
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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