Empirical approaches to estimate the nonlinear dynamic responses of earth-core rockfill dams subjected to earthquake ground motions
Bibliographic record
Abstract
This study investigates three aspects of the dynamic response of earth-core rockfill dams (ECRDs) under earthquake loadings, including induced shear strain, increased fundamental period, and shear modulus reduction. A database of the recorded ground motions of ECRD cases in Japan is analyzed to carry out this study. The required response parameters as mentioned above are extracted by analyzing the acceleration time histories recorded at the dams’ crests and foundations. Subsequently, statistical analyses are performed to achieve the objectives of this study. A graph is developed describing the change in the dam’s fundamental period with the induced shear strain, and it is observed that the increase of the dam’s period is dependent on the increase in the shear strain levels. A relationship is established to estimate the anticipated levels of shear strain from the intensity measure (IM) of the earthquake signals. In the proposed predictive relationship, a new IM is used that can adequately characterize the severity of an earthquake. Finally, a curve and a range are suggested for the average shear modulus degradation of the ECRD’s core materials. The presented empirical graphs and relationships in this study are valuable tools to obtain an appropriate perception of dams’ nonlinear behaviour under strong earthquake excitations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".