Modularization Business Case: Process Flowchart and Major Considerations
Bibliographic record
Abstract
Modularization is a well-known method of enhancing project value by exporting a portion of site work to one or more local or distant fabrication or assembly shops/yards. Still, the industry is in need of additional guidance on how to more effectively exploit modularization. To help achieve wider and more effective use of modularization, the researchers and the Construction Industry Institute’s (CII) Research Team 283 develops here a new modularization business case process for developing the modularization drivers (and for determining the degree to which modularization will be implemented). The result is an optimal decision-making process as these drivers are compared with the owner’s objectives and evaluation criteria for cost, schedule, risk and other project objectives. This paper presents this new modularization business case process and lays out the major considerations that pertain to per-project phases. The findings provide guidelines along with a flowchart that should impose rigor on the decision process, helping owners and avoiding poor outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".