A Comprehensive Analysis of BIM Technology's Critical Role in Assessing Cost for Complex Dam Construction Projects
Bibliographic record
Abstract
Over recent decades, the adoption of modern techniques and advantageous construction methods has significantly improved the construction process.Building Information Modeling (BIM) is one such critical approach that has demonstrated its considerable effectiveness in estimating cost and material quantities for large-scale projects, such as dams.This research investigates and assesses the essential role and contributions of BIM technology and associated software tools in estimating the cost of dam construction projects, characterized by their high complexity, intricate management, extended construction period, and substantial concrete and steel material requirements.A mixed-methods study incorporating three primary strategies was employed: (A) literature review, (B) quantitative research, and (C) qualitative research approaches.Data were collected through semi-structured interviews and an online survey questionnaire.The key findings from this study's analysis (using an Iraqi dam as a case study) indicate that the implementation of BIM technology and software concepts is highly advantageous, dynamic, and effective in evaluating construction project budgets.Furthermore, the research highlights that accurately estimating the cost of dams can significantly reduce the time, financial investment, and effort needed to assess the budget of construction projects, particularly those involving dams with higher complexity, extended construction periods, challenging management, and intricate activities and tasks.Additionally, the use of BIM approaches was found to substantially mitigate human error in cost estimations and enhance the performance and accuracy of dam cost evaluations.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".