Shear modulus reduction and damping ratio curves for earth core materials of dams
Bibliographic record
Abstract
It is essential to obtain shear modulus reduction and damping ratio curves to perform dynamic analyses of earth-cored embankment dams. Many studies have been performed for dynamic properties of clayey soils, but they have been limited for earth core materials of dams. This study conducted resonant column tests to obtain shear modulus reduction (G/Gmax) and damping ratio (D) curves for 31 specimens (17 undisturbed and 14 remolded specimens) from 13 earth-cored embankment dams. Empirical G/Gmax and D curves are proposed for dynamic properties of clayey earth core materials. Fitting curves are provided by using the functional forms of the Ramberg–Osgood and Darendeli models. The observation shows that the undisturbed earth cores yield relatively higher G/Gmax and lower D curves than the remolded cores. G/Gmax curves of compacted earth cores are relatively higher than those of Vucetic and Dobry curves for a similar level of plasticity index. Uncertainty and bias are calculated by performing residual analysis, which shows that there is no clear bias in predicting G/Gmax and the uncertainties between undisturbed earth core materials and natural deposits are at a similar level. A proposed empirical relationship of G/Gmax and D curves for earth core materials can be utilized for dynamic analyses of embankment dams for cases where there is insufficient in situ data.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".