A Framework for Design Waste Mitigation in Off-Site Construction
Bibliographic record
Abstract
The recent global pandemic has presented unprecedented challenges to the construction industry's survival.Therefore, even minor improvements and the elimination of small sources of waste are crucial.Although they constitute a small percentage of total construction costs, hasty designs and design errors have the potential to be one of the most significant sources of waste within the industry.Also, offsite construction involves a high degree of precision and efficiency.Any waste during the design process can result in time delays, cost overruns, and suboptimal final product performance.The design process should aim for minimal waste to avoid potential delays or errors during construction or manufacturing that could lead to wasted resources and money.To address this challenge, a framework based on lean principles has been developed to minimize waste during the design process for offsite construction.The primary objective is to incorporate lean principles and tools to address waste reduction quantitatively and measurably.Proposed solutions aim to eliminate or reduce these activities, and a framework is presented to guide organizations in mapping out the necessary steps.To assess the recommended interventions, statistical analysis and simulation methods are introduced.The framework is intended to help evaluate processes and increase efficiency during the design phase for off-site construction and built-to-order companies.The innovation of this framework lies in its precise procedures and guidance for improving these phases using Lean tools, which could provide significant benefits for off-site construction and built-to-order companies.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".