Utilization of Nonlinear Finite Elements for the Design and Assessment of Large Concrete Structures. II: Applications
Bibliographic record
Abstract
This second-part article presents applications of advanced nonlinear finite-element analysis for the design of large reinforced-concrete structures. Because shear and the size effect are fundamental aspects of these structures, the first section of this paper is devoted to the prediction of shear failure for very large members more than 3 m deep. It is shown that the tendency of shear strength is much less sensitive to size effects for very large members than the predictions of some design code equations. Applications to the draft tube complex structure are then presented in a second part. A comparison of cracking patterns with an existing powerhouse is performed at the service level. It is shown that thermal effects have an important effect on the final cracking pattern. The draft tube model is then analyzed up to failure. Following a new design methodology proposed by the authors in a previous paper, and using the model error properties computed in part 1, the global resistance factor is computed for the ultimate level. The effects of temperature, nominal shear reinforcement, and lateral confinement are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".