Modular Coordination—Architectural Design Associated to a BIM Application
Bibliographic record
Abstract
To optimize the design and execution of an enterprise, the first design solutions can be defined based on the concept of modular coordination associated with the interoperability and efficiency of BIM tools. The objective of this paper was to apply the concept of modular coordination associated with BIM in the architectural design process of a residential condominium, to explore the possibilities of project customization. To this end, two work fronts were adopted, the development of the design flexibility and customization process and the development of an application on the BIM platform. As a result, the modular coordination applied to the design and customization, associated with the customization of the BIM tool, proved to be viable, considering the expectations of the company studied about the rationalization of the design and execution processes. The customization of the space by the client added social value to the project from the beginning, and the predefinition of the finishing and construction system added economic and environmental value to the project. It was also verified that the use of modular coordination in the development of the architectural project of a residential condominium generated several benefits, highlighting the flexibility and customization of the project for each residence, the viability of using the BIM tool, and the rationalization of the construction process.
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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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".