Exploring the Influence of Risks in BIM Implementation: A Review Exploring BIM Critical Success Factors and BIM Implementation Phases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The adoption of building information modeling (BIM) has a strong potential to influence project performance positively. However, the implementation and use of BIM also involve challenges and risks that must be considered for its practice's success. This study aims to identify gaps and future research direction within the field of BIM and risk management. Besides, it explores the relationship between risks related to BIM implementation and project success dimensions. For this, a literature review is applied, merging bibliometric and content analysis. The results show that the three most frequently mentioned risks are technological interface among programs, followed by interoperability issues, and inadequate knowledge or expertise. Besides, insights pinpoint the positive relation between the BIM critical success factors and the risks associated with BIM, particularly in the design phase.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it