Managing Cost and Schedule Evaluation of a Construction Project via BIM Technology and Experts’ Points of View
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research examines the beneficial contributions of project management to manage the cost and schedule of construction projects providing higher accuracy and reliability levels. The study depended on two main research approaches quantitative (online survey questionnaires) and qualitative (interviews) research techniques. The questionnaires and interviews aim to predict the importance of BIM technology in managing the cost and schedule of construction projects. Also, a third research method is employed, which represents a case study of a building, to make a comparison in terms of accuracy, effort, and budget of quantity take-off between manual (hand calculations) and numerical (REVIT Software estimations) approaches. The results revealed that adopting project management principles could significantly cut considerable budget, time, and effort to execute and complete construction projects. Also, project management is vital to prevent any financial losses or resource problems resulting from cost overruns or delays. Moreover, the results indicated that using modern project management techniques (including BIM technology and REVIT Software) could support engineers and project managers calculate the cost and schedule of their projects more accurately, avoiding errors. Besides, the results confirmed that using the REVIT software could aid project managers and civil engineers in evaluating the budget and time required to accomplish the construction project with less effort, time, and cost and provide a lower number of human errors compared with hand calculations. It was found that the manual and numerical results are greatly approximate to each other. Still, the results of the REVIT software offered more significant precision because of the accuracy in the modeling and the few engineers’ mistakes in calculating quantities manually.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it