Probabilistic analysis for large strain radial consolidation of soft soils considering creep
Bibliographic record
Abstract
Effective design of prefabricated vertical drains (PVDs) requires accurate prediction of soil consolidation behavior incorporating creep and spatial variability in soil properties. This study develops a probabilistic analysis framework that integrates random field theory, the piecewise-linear method, and Monte Carlo simulation to evaluate the long-term consolidation of soft soils with PVDs. The framework accounts for spatial variability in soil parameters, creep strain, large-strain effects, hydraulic conductivity anisotropy, soil smear, and time-dependent loading. Three routinely measured soil parameters, including plasticity index, liquid limit, and void ratio, are treated as random variables. The proposed method is validated through comparison with field measurements from a preloaded embankment site along the Sydney-Newcastle Freeway extension equipped with PVDs. Results show that the field data align closely with the high-probability density predictions from the probabilistic analysis. Sensitivity analysis indicates that increasing the coefficient of variation leads to an almost linear widening of the estimated range, while autocorrelation distances and cross-correlation coefficients exert a relatively minor influence on consolidation behavior. These findings highlight the importance of accurately estimating the coefficient of variation in a cost-effective manner during field investigations and statistical analysis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".