BIM-based building design coordination: processes, bottlenecks, and considerations
Bibliographic record
Abstract
Successful management of the building design coordination process is critical to the efficient delivery of cost-effective and quality projects. The traditional setting of design coordination, however, is inefficient and error-prone. Building information modelling (BIM) has proven valuable for increasing satisfaction with the meeting process and decreasing arguments over issues. Despite the many advantages of BIM tools, however, many design coordination issues remain undetected, design issues are poorly documented, and coordination strategies are inefficient. The objective of this study was to develop a characterization of the BIM building design coordination process, identify the bottlenecks in the current process, and provide design considerations to alleviate the bottlenecks. The bottlenecks include: outdated BIM, disconnected trades, lack of terminology, insufficient documentation, inefficient transitions across views and artifacts, unavailability of design information, information discrepancy, unfit navigation tools, and office–site disconnect. The outcomes of this research is useful for future construction projects and the software development community.
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 | 0.059 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".