Developing a Construction-Oriented DfMA Deployment Framework
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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 the construction industry has gained attention in recent years. Studies convey that the application of DfMA in construction projects can significantly enhance overall productivity. However, the literature on construction-oriented DfMA is still limited, and its application in real-life projects has been stifled due to various constraints. Following a design science research method, a systematic literature review was conducted to identify the construction-oriented DfMA implementation challenges. To address these challenges, a construction-oriented DfMA framework was theorized, verified in a project-based context, and validated through focus group discussions with off-site construction industry experts. In this study, 45 challenges were identified and categorized into eight main constraint categories: contractual, technological, procedural, cultural, commercial, geographical, financial, and technical/cognitive. The foremost challenges to the adoption of DfMA in construction projects seems to relate to the contractual and operational aspects and their associated stakeholders. This study provides insight into the challenges of implementing DfMA in the construction industry. The investigated challenges contribute to the theoretical and practice-based checklists of limitations for implementing DfMA methods and can inform future research. Finally, this paper introduces a framework for implementing DfMA and provides supporting field-based evidence for its application.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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