Best practices for BIM Execution Plan development for a Public–Private Partnership Design-Build-Finance-Operate-Maintain project
Why this work is in the frame
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Bibliographic record
Abstract
Public-Private Partnership (P3), also known as Private Finance Initiative, projects are becoming an increasingly popular procurement method. These projects are uniquely challenging as they require the collaboration of the designers, constructors and operators from the earliest stages of the project, each of whom has a particular perspective. Balancing conflicting priorities and identifying where they align is a critical step in project planning. When BIM is used in these projects, it can provide substantial benefit to the project team by facilitating the information flow between stakeholders, minimizing duplication of effort and allowing the team to make informed decisions to optimize the project over its life cycle from both a delivery and usage perspective. A wellconceived BIM Execution Plan developed at the beginning of the project with input from all stakeholders and implemented by all stakeholders supports this goal. This approach ensures that information included in the model can be used throughout the project lifecycle, avoiding re-work, and allowing the team to "begin with the end on mind" and take full advantage of this project delivery method. This paper reviews best practices for using BIM in P3 projects and presents a framework to guide the development of a life cycle BIM execution plan applicable to this context, with the analysis and prioritization of use cases, identification of element data necessary over the project life cycle, and the staged inclusion of this data within the model. As it is based on the most complex of current project delivery methods, this framework is widely adaptable and can be used for the full range of project delivery techniques.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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