Optimizing the prefabrication finishing level in modular construction
Bibliographic record
Abstract
Prefabricated modules in modular construction projects can have a wide range of finishing levels that range from partially completed with only structural frame to fully completed with all structural, wall, mechanical, electrical, finishing and furnishing components. A higher module finishing level increases the offsite fabrication and transportation cost and decreases onsite assembly cost and duration while a lower finishing level produces the opposite results. This paper presents an optimization model that enables construction planners to identify an optimal finishing level for prefabricated modules to minimize the total cost of modular construction projects that includes all offsite fabrication, transportation, and onsite assembly costs. A case study of a modular construction project for a healthcare facility was analyzed to illustrate the use of the model and evaluate its performance. The results of this analysis highlight the original capabilities of the model in minimizing the total cost of modular construction projects by identifying an optimal finishing level for each module type in the project from a set of feasible alternatives with varying building components, weights, and cost rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".