Application of advanced bounding surface plasticity model in static and seismic analyses of Zipingpu Dam
Bibliographic record
Abstract
The strong ground motion of the Wenchuan earthquake that hit the Zipingpu Dam in China in 2008 has provided an excellent benchmark to study the behaviour of large modern rockfill dams subjected to seismic loading. The performance of the dam during construction and prior to and after the earthquake loading has been recorded with good accuracy, and provides a reliable database to examine the reliability of available constitutive models and numerical methods in predicting the static and dynamic behaviour of embankment dams. In this paper, an advanced bounding surface plasticity model has been used in a series of numerical analyses to study the static and dynamic behaviour of Zipingpu Dam. The model can take into account particle breakage that may occur in monotonic and cyclic loading of rockfill materials. The material parameters required for the model are calibrated based on the results of available monotonic and cyclic triaxial tests. In the numerical procedure, the staged construction of the dam and the subsequent impounding of the reservoir are simulated, followed by dynamic loading. At each stage, the results of the numerical analysis are compared with in situ monitoring records of the dam. The results of the numerical simulation and the displacements measured after the earthquake are also compared with those estimated by two simplified engineering procedures that are routinely used in practice. The effectiveness and applicability of the simplified procedures to such a large dam subjected to an earthquake with a long duration is also 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".