A Deviatoric Softening Model to Simulate Compressibility Properties of Soft Clays
Bibliographic record
Abstract
Soft clays are often associated with high compressibility due to their high void ratio, low shear strength, and creep behavior. Structures built on top of it can experience excessive settlement issues over a long period of time. The prediction of these settlements has attracted attentions from many researchers for over a century, but accurately predicting them still remains a difficult issue due to complex properties of soft clays, including plasticity, viscosity, anisotropy, soil structure and so forth. Therefore, studying the compressibility of soft clay is of significant importance. This dissertation aims to investigate the influence of plastic deviatoric strains on the compressibility of soft clays. First of all, the dissertation reviews a number of published incremental anisotropic consolidation tests on Finnish clays. The results demonstrate the dependence of soil compressibility on stress ratios. Based on the result, a modified yield surface size deviatoric softening law has been introduced. This softening law describes yield surface softening to be related to plastic deviatoric strain increments. Secondly, a new model named MEVP-DS, has been incorporated into the framework of Yin’s elaso-viscoplastic model to consider deviatoric softening, destructuration, and yield surface anisotropy of soft clay. Furthermore, the verification of MEVP-DS has been done through three phases. Phase one is the simulation of published incremental anisotropic consolidation tests on intact Finnish clay samples. The model results demonstrate MEVP-DS successfully captures the soil compressibility in response to different stress ratios. Phase two is the application of MEVP-DS in modeling 1-D consolidation tests on sensitive Champlain Sea clay. Model results highlight that using MEVP-DS is beneficial for predicting the compressibility and excess pore pressure response of the clay subject to constant rate of strain loading. Phase three is the application of MEVP-DS in simulating a real embankment dam on Champlain Sea clay. MEVP-DS not only simulates 40-year settlement measurements of the dam reasonably well, but also improves the prediction of lateral spreading of the dam. In summary, the MEVP-DS model proposed in this dissertation has shown to improve the simulation of soil compressibility of soft clays subject to 1-D, anisotropic and more complicated loading conditions.
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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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".