Exploring the Challenges and Opportunities of BIM Implementation in Major Architectural Projects in Iraq
Bibliographic record
Abstract
The economic, environmental, and societal impact of major architectural projects highlights the need to study the challenges that affect their performance and completion.Building Information Modeling (BIM) is a critical tool for enhancing project completion in the AEC field and has been widely used globally, especially in major projects.This research aims to explore the application status of BIM in major architectural projects in Iraq and investigate the reasons behind its limited use.The research methodology involves conducting interviews with specialists and using designed questionnaires to gather feedback from clients, consultants, contractors, and BIM practitioners.Several Iraqi major projects that implement BIM technology are selected as case studies.The research findings reveal that BIM application levels in the design and construction of major projects in Iraq are limited due to several reasons, with the main reason being the lack of government and private institutions' requirements for BIM application in their project designs and construction.This study on BIM's role and application obstacles in major projects in Iraq will serve as a guide for managers and practitioners to determine the appropriate "Level of Detailing" based on project characteristics and integration limits, enabling better benefits for owners and stakeholders.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".