Seismic Performance Analysis of the Elevated RC Tanks under Strong Far- and Near-Fault Ground Motions Considering Fluid–Structure Interaction
Bibliographic record
Abstract
The elevated reinforced concrete tanks assessed in the current work were the subject of a nonlinear sloshing analysis resulting from fluid–container interactions. The primary response quantity of interest was the height of free surface sloshing. To achieve this aim, the effect of the liquid contents on the seismic behavior of tanks subjected to various sets of far- and near-fault ground motions were measured. The variables considered in this study included bidirectional loading, the earthquake’s frequency content, water sloshing, and the three-dimensional geometry. The primary goal of this work was to analyze these crucial parameters through a parametric analysis using a finite element method considering the influence of nonlinear fluid–structure interactions under the influence of different ground motions. By contrasting the numerical results obtained by previous studies and those of the current investigation, the applicability of the current simulation in seismic analyses of the elevated reinforced concrete tanks was then examined, and significant conclusions were formed. The findings showed that the nonlinearity of sloshing may significantly affect the seismic performance of the liquid–container interactions and that failing to properly account for it may pose a severe threat to these structures’ ability to perform satisfactorily for a particular class of tanks, particularly under the influence of near-fault events.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".