Building Information Modelling (BIM) for construction project management: A literature bibliometric analysis approach
Bibliographic record
Abstract
Being a multidisciplinary sector by nature, construction projects have historically been managed in a complex, dangerous, resource-wasting, imprecise manner that has been found to increase carbon emissions. Building information modelling (BIM) facilitates simulation, collaboration among project stakeholders, and the progression of BIM from 3D spatial representation to 10D industrialized production, all of which enhance the construction project management process throughout the lifecycle of a building. Based on such precedent and benefits, one could want to do bibliometric analysis to find out how many documents have been published on BIM for construction project management. In this study, a bibliometric analysis was employed to further explore the research subject. The Scopus database (www.scopus.com) and widely available tools were used to generate and analyse 246 published documents. Data obtained from the Scopus database was uploaded into the VOSviewer software to conduct further subject-matter analysis to delve deeper into particular documents received from Scopus. Utilizing data retrieved from Scopus, clusters networks analyses of ranking, co-authorship, co-occurrence, co-citation, citation, and bibliography are created and uploaded to the VOSviewer (www.vosviewer.com) tool. Findings reveal that the top countries for literature research and the cluster network of BIM for construction project management publications are the United States, the United Kingdom, Italy, China, Australia, India, Taiwan, Canada, France, Malaysia, and Iran. stating that African scholars need to formalize more of their writings and strengthen collaboration with other industrialized nations on this topic. Keywords: Building Information Model (BIM), Construction Project Management, Bibliometric Analysis, Vosviewer, Visualisation, Network.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.208 | 0.250 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".