Integrating Computational Design to Improve the Design Workflow of Modular Construction
Bibliographic record
Abstract
The construction industry’s productivity has stagnated since the 1960s while in the same period, manufacturing and technology industries have seen vast improvements. The construction industry is coming under increased pressure to provide better value through improved quality and performance and as a result developers and constructors are looking to alternative forms of construction. In a process that borrows concepts from the manufacturing and technology sectors, such as automation and 3D modelling, Prefabricated Volumetric Construction (modular construction) is a highly versatile approach with the potential to deliver substantial cost savings, faster project delivery times, higher quality construction with less waste and emissions, and an increase in worker safety. This paper will explore the integration of computational design into the design workflow of modular construction through several project examples. The following topics will be covered: creation and assembly of parametric modules in the Revit Building Information Model (BIM) software, using the visual programming tool Dynamo; the linkage of the BIM to the analytical model; and the extraction of results and the manipulation and display of data using Grasshopper, a visual programming language and environment plugin, for Rhinoceros a 3D modeler. This integration has been found to reduce the time required to develop the building model, the drawings, the analytical model and complete the design while improving the consistency and accuracy of all.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".