Large-Strain Consolidation Analysis Accounting for Time-Dependent Effects in Clayey Tailings and Soft Soils
Bibliographic record
Abstract
Soft soils and clayey tailings exhibit time-dependent behavior due to factors like creep, structuration/destructuration which can significantly influence consolidation processes.This thesis highlights the significance of accounting for time-dependent behavior in consolidation analysis in soft soil scenarios and presents a comprehensive constitutive model that integrates these timedependent effects into a unified framework.The research begins by investigating the influence of creep alone on large-strain consolidation behavior in soft soils and clayey tailings.Four different creep models, three of which are well-established elasto-viscoplastic models named Yin-Graham model, Vermeer model, and overstress model, along with the empirical CONCREEP model, were integrated into the large-strain consolidation analysis.These creep-consolidation formulations were applied to simulate the observed consolidation in two different studies, providing the limitations and advantages of each model.The comparison of different creep models revealed that the Vermeer and Yin-Graham models show similar predictions, while the overstress model produces distinct results.The Yin-Graham model, however, exhibits limitations in infinite time creep strain, potentially overestimating long-term settlement.The overstress model is sensitive to the initial preconsolidation stress, and CONCREEP model requires fixing parameters for the final compressibility function suggesting the need for further improvements in its applicability.The creep-consolidation results underscore the need for a more comprehensive approach to address time-dependent behavior.Subsequently, a novel aging model is proposed, combining creep, structuration, and destructuration effects.The aging model simulates the evolution of soil structure, impacting creep rate, compressibility, and preconsolidation pressure.Seven different experiments are simulated using the aging model, providing a comprehensive range of aging model parameters and insights into time-dependent behavior.Furthermore, consolidation analyses are performed using three different models, including the large-strain consolidation model, Yin and Graham model, and aging model, for two different studies.The results demonstrate that considering creep and aging improves predictive accuracy, particularly for settlement and excess pore-water pressure.The aging model outperforms the other models in capturing observed compressibility over time and predicting undrained shear strength.'
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".