Improvement of a hypoplastic model for sand under undrained loading conditions
Bibliographic record
Abstract
An important drawback of the hypoplastic model is the inaccurate prediction of the sand behavior under undrained monotonic loading conditions. The model is not able to reproduce the limited liquefaction type response widely observed in undrained tests on loose sand, and it often underestimates the initial stiffness and hardening rate of sand during the shearing. To address these issues, three novel modifications are introduced into a basic hypoplastic model to enhance its undrained predictive capability. Firstly, a new factor is added to the nonlinear term of the model, allowing the simulation of a purely elastic response at the beginning of loading. By doing so, the model can accurately capture the initial stiffness and undrained effective stress path of sand. Secondly, the characterized void ratios are related to an evolving state variable, enabling the model to reasonably reproduce the limited flow response and quasi-steady state. Furthermore, a new term is incorporated into the deviatoric part of the strain rate to adjust the hardening rate of the model. The model performance for undrained loading is significantly improved through the above modifications, as evidenced by the good agreement between simulation results and experimental data for tests with varying densities and confining pressures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".