A DfMA-Incorporating Parametric Design Approach for Enhancing Constructability in BIM-Enabled Architectural Design in Off-Site Construction

Abstract To enhance constructability in building information modeling (BIM)-enabled off-site construction (OSC) projects, integrating manufacturing, logistics, assembly, and installation demands into the architectural design stage early on is crucial. Design for manufacture and assembly (DfMA) offers a structured framework to achieve this, but often is constrained by its generality and lack of practical guidance for early design. This disconnect leads to design and manufacturing misalignments and missed optimization opportunities. To address this challenge, this study proposes a DfMA-incorporating parametric design approach that translates DfMA principles into explicit DfMA-specific design information and constraints, enabling their integration into BIM-enabled parametric modeling. Through a design science research (DSR) methodology, a DfMA-incorporating parametric design framework was developed. The framework employs a component-centric analysis perspective to organize ten categories of design information and constraints into three hierarchical levels—substance, intersection, and composition. A parametric workflow was implemented with Revit version 25.0.2.419, Dynamo version 3.0.3, and Python version 3.13.13 to automatically read, harmonize, and apply these parameters to optimize and update the building information models. This approach was validated in a small-scale OSC scenario, in which the optimization process optimized 24 BIM parameters related to DfMA-specific design information and constraints. The optimization results emphasize DfMA principles, including standardized and modular design and standardized connector configurations. The optimization primarily targets parameters related to typical component dimensions, component materials, and typical connector dimensions. This approach enhances constructability by embedding DfMA principles into BIM-enabled design and parametric modeling, and a BIM plug-in was developed to automate parameter optimization and support more-informed design decisions.