Exploring the Challenges in Building Information Modeling (BIM) During the Design Phase: Evidence From Cross-Country Studies
Bibliographic record
Abstract
Building information modeling (BIM) is transforming the construction life cycle. Nonetheless, there is a notable gap in research regarding the key challenges associated with BIM. This study aims to investigate the primary challenges in the design phase and their implications for project success. To address these objectives, cross-country case studies were conducted in four large engineering companies from the USA, Canada, Brazil, and United Arab Emirates. Data were collected through 23 semi-structured interviews with managers, engineers and directors, and content analysis was performed using NVIVO software. The resulting coding structure revealed the following categories: organizational and cultural issues, professional and knowledge issues, technological and operational issues, cost issues, BIM specific issues, design issues, data issues, and information and communication issues. The findings highlighted the most significant challenge as the lack of BIM knowledge or expertise. Additionally, an important enabler in the design phase is the accuracy of data provided by BIM, which enhances project management analysis. Finally, the BIM challenges and enablers influence various benefits dimensions, particularly on the efficiency.
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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 | 0.067 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".