Building Information Modelling‐ (BIM‐) Based Generative Design for Drywall Installation Planning in Prefabricated Construction
Bibliographic record
Abstract
In prefabricated construction, building components are manufactured off‐site before shipping to the site for installation. Accurate design and planning are essential for smooth on‐site execution and improved efficiency, which requires evaluations of various design options. However, due to the design process’s complexity, such evaluations cannot be achieved without automation and optimization. Meanwhile, the recent advancement of digital design technologies (e.g., building information modelling (BIM)) has enabled flexibility in the design process. The integration of BIM with other analytical algorithms also allows optimization of designs, such as the generative design that can parametrize the design. This study proposes a generative design approach that utilizes the optimization of the drywall installation layout to improve overall project efficiency. The framework includes a decision support module that considers environmental, cost, and aesthetic aspects to identify the optimal layout. The framework’s practical applicability has been successfully demonstrated through a case study. After implementation, three “best” design alternatives were found according to the decision aspects. The design improvements achieved were 37.5%, 7%, and 54% for the environmental, cost, and aesthetic factors, respectively. Accordingly, practitioners can make better decisions on planning drywall projects. This approach has proven effective in planning drywall installation and can be applied in similar design scenarios for other prefabricated construction processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".