Analyzing BIM topics and clusters through ten years of scientific publications
Bibliographic record
Abstract
There has been considerable interest in Building Information Modeling (BIM) research and development during the last decade. BIM has established itself as a field of research in (and beyond) scientific communities interested in information technologies in construction. Interestingly, the contours of BIM as a scientific field are still not clearly identified. Several studies have recently tried to analyze different aspects of this issue, without providing a systematic and comprehensive methodological approach to accurately define the major themes and clusters of the BIM domain. This paper uses a systematic literature review approach to map the BIM research themes and clusters over ten years of scientific publications. 1244 articles published in peer-reviewed journals between 2007 and 2016 were selected and the associated metadata analyzed in order to highlight co-occurrences in author’s keywords. It appears that a few “big players” dominate the keywords, while most of the keywords used by authors are much less cited. Seven core clusters are identified using modularity optimization techniques: industry foundation classes, information technology, facility management, building, collaboration, computer aided design, and laser scanning.
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.
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 | 0.000 |
| 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 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".