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Evaluating Modular House Construction Projects: A Delphi Method Enhanced by Conversational AI

Autor(en): ORCID
ORCID

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 6, v. 14
Seite(n): 1696
DOI: 10.3390/buildings14061696
Abstrakt:

This study focuses on evaluating modular house construction projects, which is a critical segment within sustainable building practices. Despite the significant advantages of modular construction, such as enhanced resource efficiency and reduced environmental impact, existing research often overlooks its unique attributes and constraints. Our objectives were to identify crucial parameters for a comprehensive evaluation of modular construction, particularly emphasizing sustainability, and to explore how an advanced conversational AI tool, ChatGPT, can assist in modular building assessments. We employed the Delphi method to define these parameters and integrated ChatGPT to develop a robust assessment methodology. This approach allowed us to harness AI-driven insights to enrich the evaluation process. Our findings suggest that ChatGPT delivers high-quality results comparable to those produced by experts in modular building assessments. ChatGPT formulated a detailed description of the evaluation scale for each criterion, effectively outlining the guidelines for evaluating modular house projects. To illustrate the effectiveness of our proposed methodology, we applied it to a real-world modular house project in Lithuania, demonstrating how this approach can significantly contribute to advancing sustainable construction practices.

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

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10787959
  • Veröffentlicht am:
    20.06.2024
  • Geändert am:
    20.06.2024
 
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