Cone Penetration Test-Based Correlations to Forecast Critical State Parameters
Bibliographic record
Abstract
In critical state soil mechanics, the critical state refers to the combination of effective stress and void ratio (e) at which a soil continues to shear with no change in effective stress, shear stress, and e. The phenomena can be visualized using the critical state line (CSL). The CSL represents the locus of e at critical state with effective mean stress (σ′mean). To define the CSL, the CSL slope (λ), termed “compressibility,” and CSL y-axis intercept at 1 kPa (Γ), termed “altitude,” are required. The CSL in e – σ′mean space provides a simple model of complex soil behavior that allows engineers to construct constitutive models using the state parameter (ψ), which is the mathematical difference between the in-situ e of the soil and the e of the soil at critical state. Currently, Γ can be obtained only through laboratory testing, while λ and ψ can be obtained via laboratory testing or correlation. This paper presents forthcoming correlations based on the ΔQ soil behavior index (which is obtained via the cone penetration test, CPT) to forecast Γ, λ, and ψ, and compares the ΔQ-based correlations’ performance to other CPT-based correlations as well as to data obtained from literature. To compare the correlations, the authors used data from a site investigation performed in Fraser River sand as part of the Canadian Liquefaction Experiment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".