SUSTAINABILITY OF OFF-SITE CONSTRUCTION: A BIBLIOMETRIC REVIEW AND VISUALIZED ANALYSIS OF TRENDING TOPICS AND THEMES
Bibliographic record
Abstract
ABSTRACT Off-site construction (OSC) involves the fabrication and assembly of building components in a purpose-built factory which are then transported to the job site for final installation. OSC has proven to be a greener construction approach, spurring research towards benchmarking the sustainable attributes of the technique. However, a quantitative statistical analysis of studies on OSC sustainability and a framework of the knowledge domain are not well-established. Drawing on 642 bibliographic records from Scopus, this paper conducted a bibliometric and visualized analysis of research on the sustainability of OSC from 1971 to 2019. The findings show that research publications on OSC sustainability only witnessed steady growth since 2000. A geospatial analysis revealed that at least 32% of countries are involved in the OSC sustainability research, of which the United States, China, Australia, the United Kingdom, and Canada make the greatest contributions. The hot topics in the contemporary OSC sustainability research were identified as embodied carbon, embodied energy, construction waste, post-occupancy evaluation, resources conservation, and recycling, and cost savings. The paper identified areas that require further research. Thus, the paper offers an all-embracing understanding of the core research themes, trends, and patterns on OSC sustainability to 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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.139 | 0.150 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".