Design–Build Project Administration Practices for the Water Industry
Bibliographic record
Abstract
Water utilities are often required to continue building and improving their water infrastructure assets to meet increasing demands. Innovative project delivery methods, such as design–build (DB), integrate the design and construction phases and are implemented by water utilities to further advance the delivery of their water transmission pipeline projects. The objective of this study was to present effective DB project administration practices specifically for the water industry. An elaborate interview protocol was developed to collect information from utilities that have extensive experience delivering DB water projects. Interviews were conducted with 10 utilities in the United States and Canada. DB practices were examined under seven key DB project administration categories. A selection of effective DB project administration practices revealed by this study include (1) using a guaranteed maximum price (GMP) and finalizing it later in the design process; (2) providing the risk register to the design–builder to maintain during the design and construction process; and (3) using qualification-based procurement. This study contributes to the literature by identifying a wide selection of effective DB project administration practices that may support utilities in the delivery of their water infrastructure projects.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".