Analysis of Critical Factors and Strategies for Implementing and Using BIM in the Public Sector
Bibliographic record
Abstract
Building Information Modeling (BIM) is a widely adopted technology in the Architecture, Engineering, and Construction (AECO) sector, with a commercial applicability of over 20 years. However, it has not yet reached all sectors and professionals within the industry. Given this reality, this work aims to identify successful strategies and critical factors reported in global public sector experiences of BIM implementation and usage, to pinpoint the necessary approaches for its development. A systematic literature review was conducted with a qualitative-quantitative approach to achieve this. The results highlight that factors related to cultural change and training are the most critical, along with integrating technology into processes, the lack of BIM standardization, and a lack of government incentives. In light of these findings, it is understood that BIM is predominantly used for modeling, and there are still gaps in understanding the technology's use for information management. This research also presents correlations between the factors identified by authors, associating them with suggested or implemented strategies in successful experiences. These contributions can serve as the basis for further studies on maturity diagnosis or assist in formulating future BIM implementation strategies.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".