Multi-Tiered Project Delivery Systems Selection for Capital Projects
Bibliographic record
Abstract
This paper describes a method for selection of most suitable project delivery systems for capital projects. It expands upon the method advanced by the Construction Industry Institute (CII) in 2003, and incorporates additional decision criteria and project delivery systems in a multi-tier decision computational platform. The paper integrates the analytical hierarchy process to alleviate the inherent subjectivity associated with the assignments of relative weights to selection criteria used in the CII method. It also expands the range of project delivery options to include Public Private Partnership (PPP) and Integrated Project Delivery (IDP). The range of selection criteria was expanded by 60, beyond the 20 criteria of the CII method. Relative effectiveness values are proposed for the added project delivery systems making use of recent project cases in Canada and the USA. The method was implemented in a spreadsheet application. Multiple scenarios were considered for one of the cases presented in the CII study and a sensitivity analysis performed based on the developments made in this paper. The differences in outputs between the CII method and the proposed method are discussed. This is the first decision framework that incorporates both the presently used PPP and the recently introduced IDP, along with the widely used project delivery systems. The developed method allows users to filter out the factors and alternatives that do not apply to the case at hand, based on key inputs at the upper tier. The method is flexible and can easily be expanded upon and customized by the user.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".