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A GPT-Powered Assistant for Real-Time Interaction with Building Information Models

Auteur(s): ORCID


ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Buildings, , n. 8, v. 14
Page(s): 2499
DOI: 10.3390/buildings14082499
Abstrait:

This study introduces DAVE (Digital Assistant for Virtual Engineering), a Generative Pre-trained Transformer (GPT)-powered digital assistant prototype, designed to enable real-time, multimodal interactions within Building Information Modeling (BIM) environments for updating and querying BIM models using text or voice commands. DAVE integrates directly with Autodesk Revit through Python scripts, the Revit API, and the OpenAI API and utilizes Natural Language Processing (NLP). This study presents (1) the development of a practical AI chatbot application that leverages conversational AI and BIM for dynamic actions within BIM models (e.g., updates and queries) at any stage of a construction project and (2) the demonstration of real-time, multimodal BIM model management through voice or text, which aims to reduce the complexity and technical barriers typically associated with BIM processes. The details of DAVE’s development and system architecture are outlined in this paper. Additionally, the comprehensive process of prototype testing and evaluation including the response time analysis and error analysis, which investigated the issues encountered during system validation, are detailed. The prototype demonstrated 94% success in accurately processing and executing single-function user queries. By enabling conversational interactions with BIM models, DAVE represents a significant contribution to the current body of knowledge.

Copyright: © 2024 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10795561
  • Publié(e) le:
    01.09.2024
  • Modifié(e) le:
    01.09.2024
 
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