Uncovering key themes in modular construction waste management and exploring their impact and centrality
Bibliographic record
Abstract
• Quantifies prefabrication strategies in C&DW studies using text mining. • Co-occurrence analysis elucidates key themes in sustainable modular construction. • Interest in recycling persisted from 2014, especially concrete, from 2018 to 2022. • Impact-Centrality assessment identified RAC as a high-impact theme. • Cluster analysis unveils the nexus between RAC and mechanical properties. Modular construction, encompassing prefabrication and off-site construction, presents compelling benefits over conventional building techniques, notably in mitigating material waste, expediting project schedules, and reducing environmental footprint. The current study investigates 118 studies from 1,843 potential publications to identify the thematic evolution and knowledge gaps in modular construction waste management from 1996 to 2024 using combined text-mining techniques. Network mapping and node analysis using Biblioshiny and SciMat tools provide thematic development and centrality insights. Trend analysis demonstrates a significant increase in research activity post-2015, following the establishment of the United Nations Sustainable Development Goals. Co-occurrence analysis using VOSviewer identified key themes and their interrelations. Cluster analysis further delineated key themes, showing the dominance of topics such as "performance," "mechanical properties," and "recycled aggregate concrete (RAC)". We found that "Reuse" and "Recycling" themes exhibit lower occurrences and link strengths. Additionally, a Sankey Diagram visualizes interrelationships between key themes, references, and contributing countries, notably highlighting contributions from China (34%) and Spain (21%). Further findings reveal a sustained interest in recycling from 2014, particularly recycled concrete from 2018 to 2022, underscoring the adoption of off-site construction to mitigate waste and incorporate recycled materials. Impact-centrality analysis identifies "RAC" as a high-impact theme, following "Prefabrication" and "Sustainability." Network analysis highlights that the mechanical properties of RAC are of considerable interest and concern. The adopted text-mining approach provides a comprehensive view of thematic developments and identifies knowledge gaps, aiding researchers in addressing current waste challenges and developing evidence-based waste policies.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".