Building information modelling (BIM) and knowledge management in implementation for con-struction projects
Bibliographic record
Abstract
The widespread use of knowledge management in a wide range of applications aids in professionally and wisely streamlining the procedure to produce improved outcomes and deliverables. Knowledge management is essential in the construction sector due to the considerable investment, extended deadlines and the need for higher performance efficiency and quality. The usage of BIM in construction projects has simplified the procedures required in building construction. The fundamental motivation behind the creation of BIM was the need to apply knowledge management approaches to make projects more sustainable and compliant with green building requirements. There are various limitations and risks associated with using BIM in construction projects, such as issues with file sharing and data security. This research examines numerous BIM benefits and drawbacks from a variety of angles, providing information and various measures for building the program. BIM adoption in construction projects includes a wide range of benefits as well as drawbacks. The relationship between the variety of information and outcomes was explored using different findings from numerous scientific studies that looked at the use of BIM in the construction sector. Due to the wide range of data that is available in the BIM sector, numerous areas were also found in this study. A thorough analysis was undertaken to present a variety of author viewpoints from various articles. This report's strengths and weaknesses have been noted. It has been demonstrated that BIM has additional benefits, including meeting client expectations, reducing design errors, and achieving project sustainability. However, there are also drawbacks to BIM, including user skill gaps, ambiguous standards and protocols, and transfer data problems. A few studies have addressed some of the research gaps and limitations in some of the domains. Additionally, these regions were thoroughly addressed from many authors' points of view.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".