Building Information Modelling- (BIM-) Based Generative Design for Drywall Installation Planning in Prefabricated Construction
Author(s): |
Jose Daniel Cuellar Lobo
Zhen Lei Hexu Liu Hong Xian Li SangHyeok Han |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Advances in Civil Engineering, January 2021, v. 2021 |
Page(s): | 1-16 |
DOI: | 10.1155/2021/6638236 |
Abstract: |
In prefabricated construction, building components are manufactured off-site before shipping to the site for installation. Accurate design and planning are essential for smooth on-site execution and improved efficiency, which requires evaluations of various design options. However, due to the design process’s complexity, such evaluations cannot be achieved without automation and optimization. Meanwhile, the recent advancement of digital design technologies (e.g., building information modelling (BIM)) has enabled flexibility in the design process. The integration of BIM with other analytical algorithms also allows optimization of designs, such as the generative design that can parametrize the design. This study proposes a generative design approach that utilizes the optimization of the drywall installation layout to improve overall project efficiency. The framework includes a decision support module that considers environmental, cost, and aesthetic aspects to identify the optimal layout. The framework’s practical applicability has been successfully demonstrated through a case study. After implementation, three “best” design alternatives were found according to the decision aspects. The design improvements achieved were 37.5%, 7%, and 54% for the environmental, cost, and aesthetic factors, respectively. Accordingly, practitioners can make better decisions on planning drywall projects. This approach has proven effective in planning drywall installation and can be applied in similar design scenarios for other prefabricated construction processes. |
Copyright: | © 2021 Jose Daniel Cuellar Lobo et al. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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10625370 - Published on:
26/08/2021 - Last updated on:
17/02/2022