Scientometric Analysis for Cross-Laminated Timber in the Context of Construction 4.0
Bibliographic record
Abstract
Cross-laminated timber (CLT) has been one of the principal materials in mass timber construction, and now it is possible to find mid-rise and high-rise projects around the globe. This study makes a scientometric review comparison between CLT and the impact of the fourth industrial revolution (formally known as Industry 4.0) in the construction industry, focusing on worldwide academic publications between 2006 and 2022. The analysis considers keywords, co-author, co-citation, and clustering analysis. This study used 1320 documents, including journals and conference proceedings from the Scopus database, where 753 were for cross-laminated timber and 567 for Industry 4.0. Key researchers, research institutions, journals, publications, citation patterns, and trends are some of the results obtained from the scientometric analysis. Once the knowledge mapping was conducted for both fields, scrutiny of the interconnection of both areas was performed to find possible research gaps from a manufacturing perspective. Among the conclusions, it is logical to say that Industry 4.0 implementation in cross-laminated timber is still in its infancy. One of the most popular technologies impacting construction is the digital twin concept; however, no work is reported for CLT on this topic. Additionally, digital automation is a necessity in any research practice, and the use of industrial robots is shown to be an essential asset for CLT as these robots can handle complex shapes.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.156 | 0.226 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".