Activity Overlapping Assessment in Construction, Oil, and Gas Projects
Bibliographic record
Abstract
Increasing demand for project completion in a shorter duration has led to various methods of schedule compression techniques, that a well known among them is fast tracking that refers to overlapping activities that normally would be done in sequence. Fast tracking (overlapping) can reduce the execution time of a project while at the same time can result in rework, extra costs and increased risks as evident by many construction projects. In order to limit the risks of fast tracking and to gain maximum advantage, a tradeoff between benefits and losses of activity overlapping is required. Such a tradeoff requires a systematic and practical approach. For this purpose an analysis has been conducted over the inherent nature of overlapping to develop a new framework for optimizing activity overlapping in construction projects. The main objective of this paper is to introduce such a framework and the hypothesis behind it. The paper will also include the theoretical justification of the suggested framework to verify that the framework can be both reliable and practical to be used by project managers to find the optimum overlapping between activities to reduce the negative risks including sources of cost overruns and time delays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".