Construction-Oriented Architectural Design in Off-Site Construction Towards Lean Construction and Management
Bibliographic record
Abstract
The rapid development of Off-site Construction (OSC) has closely linked it to Lean Construction (LC). The importance of design has been highlighted in LC as the primary means to produce value to clients. However, the adoption of lean thinking is still modest in design. Although most research target the lean design theories and their adoption in LC or OSC, there is limited understanding of terminological and cross-sectoral problems without sufficiently considering the different contexts (incompatibilities) among manufacturing, construction, and architecture, hindering the effective use of lean thinking in the design stage, especially in architectural design. This paper clarifies the OSC architectural design methods and presents how design for manufacture and assembly (DfMA) guidelines are considered as design principles to incorporate lean thinking into OSC architectural design to achieve a constructible design towards lean construction goals in OSC projects. This study shows new insights into the cross-sectoral understanding of incorporating lean design from the manufacturing industry to architectural design. An interdisciplinary study pathway is explored focusing on coordinating lean design and Architectural design to achieve lean management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".