Design-for-Manufacturing-and-Assembly (DfMA) for the construction industry: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it