A comparison of project delivery method performance for water infrastructure capital projects
Bibliographic record
Abstract
Water and wastewater infrastructure globally is aging and in need of rehabilitation and replacement. Design-bid-build (DBB) is the traditional method of project delivery widely applied across the construction industry. However, alternative project delivery methods (APDM) such as construction manager at risk (CMAR) and design-build (DB) are on the rise demonstrating project delivery performance benefits. The research objective is to assess the impact of APDM specifically for the water and wastewater industry. A comprehensive list of performance metrics was identified from the literature and through an industry expert workshop. Information on 75 water and wastewater treatment plant projects using DBB, CMAR, and DB was collected. Quantitative data analysis revealed that DB statistically outperformed DBB in terms of project speed and intensity. This study contributed to the existing body of knowledge by showing that treatment plants can be delivered significantly faster and with greater quality for no additional cost by using APDM.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".