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.

TRANSFORMING BUILDING INDUSTRY KNOWLEDGE MANAGEMENT: A STUDY ON THE ROLE OF LARGE LANGUAGE MODELS IN FIRE SAFETY PLANNING / Ashkenazi, Ori; Isaac, Shabtai; Giretti, Alberto; Carbonari, Alessandro; Durmus, Dilan.. - ELETTRONICO. - (2023), pp. 733-742. (Intervento presentato al convegno CONVR 2023 tenutosi a Florence, Italy nel November 13-16, 2023).

TRANSFORMING BUILDING INDUSTRY KNOWLEDGE MANAGEMENT: A STUDY ON THE ROLE OF LARGE LANGUAGE MODELS IN FIRE SAFETY PLANNING

Isaac, Shabtai
;
Giretti, Alberto;Carbonari, Alessandro;Durmus, Dilan.
2023-01-01

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/325632
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