The Mechanism of Design Activity Overlapping in Construction Projects and the Time-Cost Tradeoff Function
Bibliographic record
Abstract
An effective and well known technique for earlier completion of construction projects is to overlap the project activities or phases that normally would be performed in sequence. Overlapping, also called fast-tracking, is inherently risky because it increases uncertainties and can result in more changes and rework. In order to gain maximum advantages from early project completion, a tradeoff between benefits and losses of activity overlapping is required. Such a tradeoff is a type of time-cost tradeoff. Various time-cost tradeoffs have been extensively studied in the project management and construction management literature; however, limited research exists to address the activity overlapping time-cost tradeoff. In this research, the theoretical mechanism of overlapping has been identified through a literature review and interviews with planning and scheduling experts. Then, an objective function that reflects the overlapping time-cost tradeoff is developed. The paper explains the overlapping mechanism in detail, and introduces the suggested objective function. It also shows that the most important negative impact of overlapping design activities is the rework occurring due to incomplete information exchange between activities; such rework has both time and cost impacts on overlapping and should be considered a key parameter in the time-cost tradeoff. The results of this research can generate insights for further research to solve the overlapping time-cost tradeoff and determine the optimum overlapping degree between activities in construction 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".