Influence of statistical sample size on evaluation of overall strength of cement-treated soil column
Bibliographic record
Abstract
The quality of cement-treated soil columns is normally assured based on the unconfined compressive strength q u of core samples. q u of core samples varies spatially and the statistical parameters of q u (i.e., mean µ q u , variance [Formula: see text], and autocorrelation distance θ q u ) are adopted in quality assurance procedures. The statistical parameters of q u evaluated from the core sample strengths have a statistical uncertainty depending on the statistical sample size. The present study investigates the influence of the statistical sample size on the evaluation of overall strength of a cement-treated soil column. A probabilistic framework in which a Bayesian inference analysis and a finite element method analysis are incorporated is used to calculate the overall strength while simultaneously considering the statistical uncertainty and spatial variability of core strength. The probabilistic framework is briefly described, and a parametric analysis is performed to investigate the influence of the statistical sample size on the evaluation of the overall strength of a full-scale column. The numerical results show that the sample size and spatial correlation influence the variability of the overall strength, and the influence can be reasonably described using an equivalent number of independent data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".