## 1 Introduction
The primary objective of this paper is to address this gap by presenting a systematically structured approach for capturing and transferring essential domain experts' knowledge on constructability into the computational design process of prefabricated facade panels. Furthermore, by leveraging the FBM concept, this approach aims to demonstrate how tacit domain knowledge can be transformed into explicit, machine-readable instructions. To achieve this, a new taxonomy for capturing typical constructability knowledge required for designing prefabricated facade panels has been developed, emerging from close observations in a relevant case study and supported by similar examples from related construction projects. Constructability constraints of prefabricated building components are multifaceted, as is the required knowledge to address these constraints. These constraints range from those related to the geometry of prefabricated building components to various semantic attributes, such as material and performance [17]. Additionally, the context of constructability knowledge can vary based on the type and functionality of the prefabricated building components, requiring specific additional expert input from fields such as architectural, structural, and mechanical, electrical, and plumbing (MEP) [18]. For example, the manufacturing and assembly processes for mechanical components [19] can differ significantly from those of prefabricated structural components [20], as do the constraints related to their geometries and semantic attributes. These considerations suggest that a two-dimensional categorization of constructability knowledge for prefabricated elements could serve as a feasible framework for holistic and comprehensive investigations. However, due to the limitations of this research--primarily its reliance on data from a single case study--this study does not aim to propose or validate such a framework that is universally applicable to constructability constraints in prefabrication projects. Instead, the focus is on illustrating how computational designers can practically integrate the necessary expert knowledge and systematically review design elements across different levels of constructability constraints. Therefore, the main scope of this research is capturing and integrating constructability knowledge into the computational design of prefabricated facade panels as architectural elements, as well as their manufacturing and assembly, given the limited available data to the research team. Furthermore, the research focuses mainly on complex designs with repetitive prefabricated building elements frequently used to create unique facade designs, where computational design is already commonly employed. In this way, other unrelated computational challenges, such as software interoperability, workforce computational skills, and design coordination issues, can be filtered out. In this context, the research team chose to closely study a construction project with repeating prefabricated facade components. Such facade components can generally be fabricated in varying shapes and quantities, installed onto the primary structure using different techniques, and positioned in diverse relations to one another, making them highly susceptible to the lack of adequate constructability knowledge. The methodology in this paper is based on an ethnographic case study of the Orchard Commons (OC) project on the UBC campus in Vancouver, Canada with its distinctive architectural design of facade panels. In addition to a comprehensive literature review of related works (Section 2), data collection was primarily based on direct observations of the OC project by the research team and semi-structured interviews with various project practitioners, especially the project's computational designer. The methodology in this research is detailed in Section 3. In Section 4, a series of geometric constructability features identified from the OC project is presented that should cover a variety of typical design intents and geometric constructability constraints.