Design for Manufacturing and Assembly: A BIM-Enabled Generative Framework for Building Panelization Design

Author:

Liu Hexu1ORCID,Zhang Yuxuan2,Lei Zhen3ORCID,Li Hong Xian4ORCID,Han SangHyeok56

Affiliation:

1. Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI 49008, USA

2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada

3. Offsite Construction Research Centre (OCRC), Department of Civil Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada

4. School of Architecture and Built Environment, Deakin University, Locked Bag 20001, Geelong, Victoria 3220, Australia

5. Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC, Canada

6. Centre for Innovation, Construction and Infrastructure Management (CICIEM), Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, Canada

Abstract

Offsite construction (OSC) is attracting increasing attention from both industry and academia due to its benefits, such as improved productivity and quality, as well as reduced waste. However, the current building panelization design in OSC is a time-consuming and experience-based manual process, and the generated panelization design may result in unbalanced manufacturing processes. One reason is that the prefabrication of building components involves a highly variable product mix and there is a lack of a computational framework to evaluate panelization design. The objective of this research is, thus, to propose a BIM-based generative framework that automatically generates the design of production components with the aim of improving production productivity. This framework consists of a building information extraction module, a generative design algorithm, and a simulation-based performance evaluation model. The building information extraction module is designed to extract building component information from a BIM model and classify building components into different production groups in accordance with functionalities and materials. The generative design algorithm is then developed to formulate panelization design alternatives in consideration of the structural, production, and logistics constraints. On this basis, the generated panelization designs are quantitatively assessed by a simulation-based evaluation model in terms of productivity. A case study was used to verify and validate the framework. This research contributes to the body of knowledge by a computational framework of building panelization design, which leverages the generative design algorithm and BIM-simulation integration for optimized panelization design.

Publisher

Hindawi Limited

Subject

Civil and Structural Engineering

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