A generative design approach for modular construction in congested urban areas
Bibliographic record
Abstract
Purpose Modular construction is widely adopted and used in the construction industry to improve construction performance with respect to both efficiency and productivity. The evaluation of design options for modular construction can be iterative, and thus automation is required to develop design alternatives. This research aims to explore the potential of utilizing the generative design approach to automate modular construction for residential building structures in urban areas such as New York City. Design/methodology/approach The proposed research methodology is investigated for a systematic approach to parametrize design parameters for modular construction layout design as well as incorporate design rules/parameters into modularizing design layouts in a Building Information Modeling (BIM) environment. Based on current building codes and necessary inputs by the user, the proposed approach enables providing recommendations in a generative design method and optimizes construction processes by performing analytical calculations. Findings The generative design has been found to be efficient in generating layout designs for modular construction based on parametric design. The integration of BIM and generative design can allow industry practitioners to fast generate design layout with evaluations from constructability perspectives. Originality/value This paper has proposed a new approach of incorporating generative design with BIM technologies to solve module layout generations by considering design and constructability constraints. The method can be further extended for evaluating modular construction design from manufacturability and assembly perspectives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".