ACR-1000® Constructability and Modularization
Bibliographic record
Abstract
Many industries are using skids, modules and prefabrications to enhance construction productivity, reduce schedules and thus reduce costs. The leaders in this regard are offshore oil and gas, chemical and refinery, ship building, etc. Atomic Energy of Canada Ltd. (AECL®) has had considerable success at the Qinshan Nuclear Power project in China with the use of modularization, which proved extremely effective in paralleling of activities and shortening the schedule. Extensive use has been made of skids and modules in Japan and this also has proven effective in shortening schedules in the construction of nuclear power plants. Secondary benefits of modularization and prefabrication include decreased site congestion and logistical issues, increased worker safety, and better quality control of fabrication. Modules and prefabrication allow work to be shifted to areas where skilled trades are more readily available from a site where skilled trades are very limited. One of the objectives for the ACR-1000® (Advanced CANDU Reactor®) project is to produce a design to allow for a very secure construction schedule. The construction method and strategy, consisting of extensive use of prefabrication and modularization, has been defined very early in the ACR-1000 conceptual phase of the layout and design process. This is addressed through a constructability programme that integrates the civil design with site erection and module installation. This paper presents the ACR-1000 construction strategy and methods and show examples of how the integrated civil design approach with modularization and prefabrication are utilized to shorten the construction schedule and reduce the project risk.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".