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Bim machine learning and design rules to improve the assembly time in steel construction projects

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Soh, Mathieu Fokwa, Bigras, David, Barbeau, Daniel, Doré, Sylvie et Forgues, Daniel. 2022. « Bim machine learning and design rules to improve the assembly time in steel construction projects ». Sustainability, vol. 14, nº 1.

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Abstract

Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Doré, Sylvie
Forgues, Daniel
Affiliation: Génie mécanique, Génie de la construction
Date Deposited: 25 Feb 2022 21:07
Last Modified: 03 Mar 2022 16:33
URI: https://espace2.etsmtl.ca/id/eprint/24028

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