Numerical analysis of structural components in power generation facilities
Bibliographic record
Abstract
The analysis of large civil structures made of composite materials, like brick masonry and/or heavily reinforced concrete, should best be conducted at a macro-scale. In this case, the material can be described as a continuum whose average properties are identified at the level of constituents taking into account their geometric arrangement. For structural masonry, several different approximations have been developed for assessing the average properties. Those include the micropolar Cosserat continuum models (e.g. Sulem and Muhlhaus [1], Masiani and Trovalusci [2]) as well as the estimates based on the theory of homogenization for periodic media (e.g., Anthoine [3-4]). In addition, a significant work has also been undertaken with regards to the development of phenomenologicaly-based macroscopic failure criteria. Examples include the studies of Lourenco et al. [5], Raffard et al. [6] and Ushaksaraei and Pietruszczak [7]. For heavily reinforced concrete structures, such as hydraulic or nuclear ones, the presence of reinforcement cannot be modeled in a discrete way, as this would be beyond the capabilities of modern day computers. Thus, the material should also be considered as a composite medium comprising the concrete matrix and a set of families of reinforcement. NUMERICAL ANALYSIS OF STRUCTURAL COMPONENTS IN POWER GENERATION FACILITIES
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".