A Framework for Building Design Management in an Imperfect BIM Environment
Bibliographic record
Abstract
Building design process is a wide spectrum activity, regarding its complexity. It begins by simple compartments structures till large multistory administrative buildings, involving various specialties. Complex design management, where a multidisciplinary team or teams are involved in the design process; requires high level of collaboration to reach the optimized client goals economically and on time. BIM provides a wide range support for such an activity, where the most critical criterion in design management is ensuring the appropriate flow of information between design partners. Each participant in the design process should receive relevant data or information in a complete form and on time. And consequently design participant should send and share their design outputs with relevant personnel. The perfect BIM environment requires that all participants within the design process are using BIM supporting software, and vice versa. Markets where BIM application is still in the growing phase some of the design participants adopt BIM while others don’t. The main target of this paper is to propose a collaborative scheme to ensure information flow between design participants, using COBIE forms and XBIM toolkit; in the case where some of the participants in design process are not using BIM supporting software.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".