Design-for-Manufacturing-and-Assembly (DfMA) for the construction industry: A review
Bibliographic record
Abstract
Applying Design for manufacture and assembly (DfMA) principles in building has gained attention in recent years. Studies reported that the application of DfMA in building projects can significantly enhance overall productivity. However, the literature on DfMA in the construction industry is still limited. This paper aims to provide an updated and comprehensive review of DfMA approach and its applicability in the construction industry. Web of science, and Google Scholar databases were used to obtain relevant articles from the literature. The study is based on a systematic review of 52 selected articles through search keywords for DfMA. The bibliometric results mapped the research publications by year, journal, and country in which the DfMA study is conducted. The thematic analysis results revealed the research themes and trends. In conclusion, the DfMA literature has increasingly focused on integration and sharing of information during project life-cycle to optimize design, manufacturing, and assembly, and to address issues relating to the integration of off-site manufacturing with on-site assembly. Finally, the review is concluded by providing recommendations for researchers and practitioners, and by identifying future works and opportunities for the application of DfMA in the construction industry. The results of this paper can help future theoretical and empirical research and developments.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".