Analysis framework of off-site manufacturing solutions : case study of a powerhouse complex
Bibliographic record
Abstract
The quest for improved productivity and optimized construction costs are continuously challenging the industry. The experience gained from the manufacturing industry showed that off-site manufacturing appears to be a promising alternative to the classic way of building a powerhouse complex. However, ingularity of projects, sizes of powerhouse complexes and multiplicity of stakeholders greatly impact the integration of such practices. To fully assess the benefits of off-site manufacturing, and to guarantee its integration within the project, it is essential to understand the issues in order to characterize benefits related to construction projects in remote areas. In this paper, the research explores off-site manufacturing integration in the industrial context of a major Canadian utility company. One of the goals is to reduce the duration and costs of construction of a future powerhouse complex project through the use of off-site fabrication. The objective of this research is to maximize these benefits of off-site fabrication through the identification of the best available strategies. To do this, a strategic analysis is conducted to evaluate off-site fabrication impact over current processes. Then, an economic analysis estimates the benefits of potential decisions made during the engineering phase. This research contributes to the construction industry by proposing an analysis framework for the identification of off-site manufacturing solutions in the context of a powerhouse complex project.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".