Estimating the overconsolidation ratio in uniform cohesive soil with cone penetrometer tests considering soil structure and index properties
Bibliographic record
Abstract
A basic empirical approach to estimate the preconsolidation stress and overconsolidation ratio, OCR, in uniform cohesive soils using cone penetration and piezocone tests uses a correlation factor, kc, that is equal to the preconsolidation stress divided by the net tip stress referenced to the soil plasticity index. An adaptation of the “stress history and normalized soil engineering properties” (SHANSEP) format extends this basic approach by organizing the data into normally consolidated and overconsolidated components. The SHANSEP-based approach is improved in this paper by applying an empirical method to identify structured and unstructured soil behavior and to develop a separate empirical correlation for each type of soil structure. Overconsolidated structured soils are shown to exhibit kc values less than the normally consolidated kc and the m exponent in the SHANSEP relationship is greater than 1. In unstructured overconsolidated soils, the kc value is greater than the normally consolidated kc value and the SHANSEP m exponent is less than 1. The proposed method to identify structured vs. unstructured behavior is an important improvement in the approach and helps illustrate why some mCPTu values are less than 1 and others are greater than 1. Site characterization efforts are significantly improved when structured soil behavior is identified and included in the assessment of OCR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".