BIM adoption and implementation in the civil and transportation infrastructure sector: analysis of governmental roadmaps and action plans
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
The adoption of Building Information Modeling (BIM) is accelerating in the global transportation and civil infrastructure sector. This research aims to identify and prioritize essential strategic actions proposed by public agencies for successful BIM deployment within this sector. The results can guide organizations in developing strategic documents based on an in-depth analysis of governmental roadmaps for BIM adoption and implementation. A four-staged content analysis approach – data collection, processing, analysis, and synthesis – was used to identify common actions supporting BIM adoption. A corpus of BIM adoption or digital transformation roadmaps in transportation and civil infrastructure was compiled. Based on the analysis of 20 documents, 640 actions were identified, rationalized and categorized into six key areas: 1/Management and coordination, 2/Mobilizing and developing skills, 3/Policies, contracts & legislation, 4/Processes, methods & workflows, 5/Documentation and standardization, 6/Digital ecosystem. Each action was further categorized into 19 sub-categories, highlighting key actions areas for BIM adoption in the sector as well as global trends around specific types of actions undertaken. This research is one of few efforts to map and analyze government roadmaps for BIM implementation, emphasizing the specific actions required to achieve this goal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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