Optimizing DFMA Implementation in On-Site Construction: A Decision-Making Framework
Bibliographic record
Abstract
The integration of Design for Manufacture and Assembly (DfMA) principles into On-site Construction (OnSC) introduces a multifaceted decision-making process, shaped by challenges across economic, technological, legal, cultural, and policy dimensions.This study identifies and categorizes key decision support factors (KDSFs) essential for successful DfMA implementation, addressing challenges such as cost overruns, technology adoption, accurate cost estimation, and interdisciplinary communication.Among these factors, economic considerations, particularly high initial capital costs and budget overruns, are the most critical.These challenges are mapped to seven key dimensions: project characteristics, supply chain, time, cost, quality, procurement, and socio-cultural aspects.For example, economic challenges align with cost-related factors like material pricing, whereas legal challenges intersect with procurement strategies and contract delivery methods.This research introduces a robust decision-making framework that integrates these challenges with KDSFs, offering stakeholders a structured approach to evaluate the feasibility and facilitate the effective implementation of DfMA in OnSC.By addressing potential risks and improving interdisciplinary collaboration, the framework enhances project efficiency and outcomes.The study highlights the necessity of a multi-criteria decision-making system to navigate the complexities of DfMA, paving the way for more effective application in on-site construction projects.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".