Current State of Practice in Selection and Implementation of Airport Capital Project Delivery Methods
Bibliographic record
Abstract
Selection and implementation of project delivery methods are important decisions in an airport capital project that will determine how the airport agency employs procurement and financing strategies, management structures, and cash flows. Most current delivery method selection approaches in the aviation sector rely on experience and opinions of construction experts. A research gap exists regarding understandings of existing and emerging delivery methods as well as their alignment with procurement and management structures, organizational capabilities, and financial planning considerations. The objective of this study was to investigate the current state of practice in selection and implementation of project delivery methods in airport projects. A content analysis of 26 airport projects across the US was performed to synthesize documents regarding selection and implementation of project delivery methods and procurement approaches. The results show that alternative contracting methods, including construction manager-at-risk, design-build, and public–private partnership, are increasingly used in authority/quasi-government airports because of their connection to financial planning, contract strategies, and procurement methods. This study contributes to the body of knowledge by providing a comprehensive review of current practices of selecting and implementing project delivery methods in capital airport projects. The results also imply the importance of considering risk-based methodologies for project delivery selection in the aviation sector.
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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 | 0.143 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".