Improving Hydraulic Conductivity Estimation for Soft Clayey Soils, Sediments, or Tailings Using Predictors Measured at High-Void Ratio
Bibliographic record
Abstract
Consolidation parameters are required to support the disposal management of soft soils or mine tailings. The estimation of these parameters from simple correlations using easily measured properties can be advantageous, and considerable work has been done on this topic. This paper proposes two innovations that advance this work: (1) hydraulic conductivity–void ratio (k-e) estimation can be substantially improved by using a single measured value at a high-void ratio, and (2) the compressibility curve itself can be a useful predictor of k-e. Using these findings, general equations are derived that describe k-e using a power law, where the power is either 4 or 5. Examining 79 k-e data sets from clays, clayey tailings, and dredged materials, 94% of all predicted k values are within an order of magnitude of measured k-e values. This level of accuracy, coupled with the advantage of anchoring the k-e function by a measured value at a high-void ratio, is shown to result in robust predictions of settlement in large strain consolidation analyses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".