## 2 Research Background
### Constructability Knowledge
For instance, refs. [31; 41] offer insights that combine architectural and structural expertise. Since this research primarily relies on limited data from a single case study, it does not aim to comprehensively address all aspects of the two-dimensional framework described above. Instead, its main focus is on investigating the integration of experts' knowledge related to the constructability of manufacturing and assembly processes for complex prefabricated facade panels, primarily from the architectural perspective. Constructability knowledge, and knowledge in general, can be classified as: (1) explicit knowledge, articulated in written form; and (2) tacit knowledge, which resides in the minds of domain experts [42; 43; 44]. According to [45], it is widely accepted that most constructability knowledge is tacit. Thus, there is a need to convert this tacit knowledge into explicit knowledge and systematically integrate it into the design process. According to [46], integrating constructability knowledge into the design process requires intensive exchange with general contractors and possibly with key trade contractors who will be responsible for building the structure during the design phase. More specifically, it is vital to identify computational approaches that facilitate capturing, associating, communicating, coordinating, and disseminating construction-related \"knowledge\" [47; 48]. While many studies have explored domain knowledge for constructability reviews, most have primarily focused on Knowledge Elicitation (KE) using methods like expert interviews, surveys, direct observations, and document analysis [22; 49; 50; 51]. KE is indeed a critical step, but current research often falls short of discussing how to systematically integrate elicited knowledge into technical activities, including the computational design process. Thus, while understanding constructability knowledge is fundamental, its integration into the design process requires robust computational tools and frameworks. The next section explores the existing body of knowledge on embedding constructability knowledge into computational design tools, utilizing constructability features to support the overall design process. Integration of Constructability Knowledge into the Design Process Using Building Information Models and Constructability Features  
[PERSON] et al. [26] noted that most construction projects lack formal, explicit constructability knowledge bases connecting observed constructability issues with design processes. While the use of \"informative construction methods and advanced technology\" was once seen as a barrier to constructability [52], contemporary computational solutions, especially BIM and generative design approaches, now facilitate automated scenario creation for design constructability reviews [53]. With building information models (BIMs), the AEC industry can collect, produce, manage, and utilize essential data and information interoperably throughout a building's lifecycle, as outlined in [46]. Researchers have demonstrated that BIM can bolster collaboration between design disciplines, especially between designers and constructors, to address constructability issues [54]. However, while BIM can be effective, the systematic integration of constructability knowledge into the design process demands new computational solutions beyond just BIM. Various studies have sought to fill this gap. For example, ref. [55] proposed a rule-based method for evaluating the constructability of cast-in-place concrete construction. While these rules can act as control measures for reviewing designs, they do not necessarily guide designers during the initial stages of a project. Similarly, ref. [7] introduced an automated constructability assessment of design BIMs based on various design factors, including dimensions, specifications, resources, and BIM component functions. However, like [55], this approach does not systematically introduce constructability knowledge during design activities.