Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA
Bibliographic record
Abstract
<p>The Finite Element Method (FEM) has been routinely used in geotechnical engineering. However, its realistic simulation ofsoil behavior depends on the accurate model input parameters. This study aims to determine through an inverse analysis on the compressibility of fine-grained soils in the Greater Toronto Area (GTA) according to the Hardening Soil Model (HSM). A series of oedometer test results is collected from a local transit project and back analyzed by employing UCODE, auniversal inversemodeling tool,which can adjust model parametersto fit the simulated results with the test values. First, a sensitivity analysis is performed to select the most critical model parameters to simplify the problem. Second, the selected HSM parameters are calibrated by combining UCODE with geotechnical FEM software, PLAXIS. Third, a statistical analysis is conducted on the compressibility parameters according to the soil types. In the end, a series of correlation formulas are derived to estimate the compressibility properties from soil indices.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".