Embankment on sludge: predicted and observed performances
Bibliographic record
Abstract
The case history of an embankment built over soft water-treatment sludge is presented. To assure that the sludge would consolidate and gain strength as predicted, a test embankment was built. The observed performance of the test embankment was compared with the predicted performance to verify and modify design assumptions. The results were used to design and construct the full-scale embankment. The finite element method and the critical state model were used to predict the performances of the test embankment and the full-scale embankment. Bayesian updating and system identification were used to update the material properties used in the prediction for the test embankment. The updated properties were then used to update the prediction for the test embankment and to predict the performance of the full-scale embankment. These predictions were compared with the observed performances to evaluate the accuracies of the predictions with different input data. Efforts were made to identify factors that cause differences between predicted and measured performances.Key words: Bayesian updating, consolidation, finite-element prediction, shear strength, stability, water-treatment sludge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".