Consequences of sample disturbance when predicting long-term settlements in soft clay
Bibliographic record
Abstract
An approach for assessing the effects of sample quality is presented. Soil samples were taken using a 50 mm Swedish STII piston sampler and the Norwegian University of Science and Technology (NTNU) mini-block sampler from a soft clay test site. Differences in laboratory test results are identified for several stress paths, assisted by simulations made using an advanced constitutive model. Hitherto such comparisons have focused on differences in basic engineering properties such as strength and stiffness. The effect of choosing alternative model parameters from piston and block samples is demonstrated through the analysis of the long-term settlement of an embankment. The simulations show that substantially larger settlements and lateral displacements are predicted using parameters obtained from the piston samples. Furthermore, the magnitude of the differences is larger than expected. This demonstrates that for this application, relatively small differences in the assessed sample quality, using traditional laboratory data interpretation methods, are amplified when applied to a prototype boundary value problem. It is suggested that a little more care in sampling and testing can result in large cost savings as a result of the more reliable model parameters that can be extracted, particularly when the improved sampling is combined with the use of an advanced constitutive model.
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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 | 0.003 | 0.010 |
| 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) | 0.000 | 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".