EVALUATION OF DESIGN-BID-BUILD (D-B-B) PROJECTS WITH AND WITHOUT AGENCY CONSTRUCTION MANAGEMENT
Bibliographic record
Abstract
Agency CM is a construction management system, and is a way to manage the process of construction. Agency Construction Management when paid on hourly Fee tempts to work hours that are not needed to maximize fee. When Agency CM is paid by Lump sum Fee or remunerated by a percentage of the Project Cost, Agency CM is only committed to provide defined services and results for a lump sum Fee. So the Agency CM may be tempted to provide less service than what was contracted for to increase profits and may maximize profits by cutting corners on services at risk of not obtaining expected results. Hence there is a need for the owner to understand whether there is improvement in Project Performance when Agency CM is contracted with any Project Delivery System. Design-Bid-Build (D-B-B), Design-Build (D-B), and Construction Management at risk (CM at - Risk) are the three principal project delivery systems. Agency CM can be used with any type of Project Delivery System. This necessitates a comprehensive investigation in to the performance of projects delivered with Agency CM and projects delivered without Agency CM. This study evaluated the project performance metrics such as Project Cost, Project Schedule and Project quality in Projects where Design-Build (D-B-B) Project Delivery System was used with Agency CM and Projects where Design-Bid-Build Project Delivery System was used without Agency CM. The study included literature review, designing a questionnaire, collecting data from 200 Design –Bid-Build (D-B-B) projects of which 100 projects where Agency CM was used and 100 projects where Agency CM was not used. Analysis of data pertaining to project performance metrics was done by using SPSS statistical software. An understanding of this study can help an owner/client better select the D-B-B project delivery system either with or without Agency Construction management.
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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 | 0.039 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".