A unified constitutive model for instantaneous elastic–plastic and time-dependent creep behaviour of gravelly soils under complex loading
Bibliographic record
Abstract
The behaviours of gravelly soils under monotonic, cyclic and creep loading are generally simulated in separated processes with different models, which cannot describe the interactions between the instantaneous elastic–plastic and time-dependent creep behaviours under complex loading. To address the problem, an elastic–plastic–creep model framework for gravelly soils is described to present its basic components and the differences from the elastic–plastic model framework. Within this framework, a unified constitutive model for instantaneous elastic–plastic and time-dependent creep behaviour is proposed based on the widely used generalized plasticity model. In modelling the elastic–plastic behaviour, the plastic modulus in the original model is modified to capture the creep hardening by a unified hardening state parameter for monotonic, cyclic and creep loading. In modelling the creep behaviour, the creep strain rate independent of the time variable is presented to reflect the creep behaviours under the unloading–reloading path and its difference from that of virgin loading. The proposed model can be easily implemented into the computing program as the original model with only minor adjustments. The ability to simulate the time-dependent behaviour under multistage loading/reloading-creep, different strain rates, step changes in strain rate, and stress relaxation are proven with the experiments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".