A Conceptual Approach for the Knowledge-Based Computational Design of Prefabricated Façade Panels Using Constructability Features
Bibliographic record
Abstract
The use of parametric models in the architecture, engineering, and construction (AEC) industry has made it possible to create complex and creative building designs. However, this design complexity creates major constructability issues, especially in projects that incorporate prefabricated façade panels. Computational design methods can solve some of these issues; however, such methods do not necessarily include the systematic approach to integrating domain knowledge, which results in inefficiencies in the design and construction processes. This paper introduces how constructability knowledge can be incorporated into computational design process using feature-based modeling (FBM). An ethnographic case study of a high-rise building with complex façade design is presented in this paper. The research identifies the critical geometric constraints that affect constructability and introduces a new three-level taxonomy (Micro, Meso, Macro) for classifying these constraints. The suggested taxonomy is then applied to inform developing a conceptual knowledge-based computational design approach that enables incorporating the insights of domain experts into the design process. Moreover, the research provides a range of external examples to validate the proposed taxonomy. The findings demonstrate the potential of FBM to streamline the design and fabrication of prefabricated façade panels, improving constructability without compromising architectural intent. This study provides a structured methodology that can be applied to enhance design efficiency and reduce construction risks in similar projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".