Evaluation of Construction Project’s Cost Using BIM Technology
Bibliographic record
Abstract
In this research, a case study is explored and examined by choosing a construction project.The budget of this project is calculated and analyzed using two methods.A comparison between these two approaches is conducted in terms of performance, effort, accuracy, and cost of calculation.These two techniques are manual quantity surveying and numerical project take-off depending on BIM technology (REVIT software package).The quantity take-off of steel, concrete, and other vital architectural elements and structural building components was considered.The results of this work revealed that there are perfect agreements between the traditional cost-estimation method and the ANSYS numerical calculation associated with all construction elements and components, indicating that BIM technology can offer a reliable solution to determine the construction project cost with higher complexity and components.Furthermore, it was found that the use of REVIT software has cut a significant number of human errors that occurred during the estimation process and quantity take-off for the project cost.In addition, the results of the manual and numerical methods of cost calculation indicated that the REVIT software had saved much time and effort needed for engineers to estimate the budget related to this challenging case study that represents a hospital building with various structural components.
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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 | 0.000 | 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 | 0.000 |
| 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 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".