Modeling interaction between loading-induced and creep strains of rockfill materials using a hardening elastoplastic constitutive model
Bibliographic record
Abstract
An elastoplastic constitutive model that takes into account the stress–strain relationship and creep-induced hardening behavior of rockfill materials is proposed in light of previous experimental observations. It is assumed that the mechanical response during loading and the final amounts of creep strains under a constant stress state are independent of the strain rate. The focus of the proposed model is the coupling effect between loading and creep, including the influence of loading history on subsequent creep strains and the influence of creep history on subsequent loading behavior. An extended yield function, which allows flexible control over the shape of yield surfaces, is used not only to distinguish among loading, unloading, and neutral loading, but also to manipulate the creep-induced hardening using a plastic strains–based hardening parameter. A stress-dependent dilatancy equation is used, instead of a plastic potential function, to define the directions of plastic strains during loading. The hardening law is established based on three different types of experimental results. Only routine experiments are required for calibration of model parameters, and the model can be used in a reduced form according to the available test results. The model is verified using typical experimental data and is found to be capable of capturing important behavior of rockfill materials, such as pressure-dependent strength, shear contraction and dilation, and creep-induced stiffening.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".