Losses Resulting from Exceeding the Specified Time for Completion and Its Impact on the Status of the Project and Delay in Utilizing Services
Bibliographic record
Abstract
This research is executed to explore the substantial impacts of delay problems in construction projects and highlight some novel practices and solutions that could mitigate this issue.The research implemented two study methods, including quantitative and qualitative approaches.A third research approach, representing a case study of a building in Iraq, is analyzed via REVIT quantity take-off to compare the accuracy and delay problem between hand calculations and the REVIT method.The results revealed that the delay problem caused cost overrunning, losses in resources, rework, and customer dissatisfaction.Also, the findings indicated that active communication, effective teamwork, efficient support from top management, and robust project planning are important to resolve the delay issue.Further, the use of modern delay management techniques is vital to avoid financial and resource losses.The results also indicated that the projects' activities should be tracked in accordance with their executions to help detect delays.Moreover, the results confirmed that utilizing the REVIT software for quantity take-off of steel, excavation, and concrete is greatly helpful, accurate, effortless, and could reduce significant human errors.
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 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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".