A method for correlating large penetration test (LPT) to standard penetration test (SPT) blow counts
Bibliographic record
Abstract
The standard penetration test (SPT) split-spoon sampler is too small for investigations in gravelly soils. For this reason, several researchers have developed scaled-up versions of the SPT, commonly referred to as "large penetration tests" (LPT), and attempted to correlate the measured blow counts to SPT blow counts. Several LPTs have been in use worldwide; each with different drill rods, sampler dimensions, and hammer energies; hence existing published LPTSPT correlations differ for each system. This paper summarizes the LPT data in the literature and presents a fundamental method for predicting LPTSPT correlations. The proposed method is based on wave equation analyses of SPT and LPT and considers variations in test equipment, penetration resistance, and energy. It is shown that the method provides a unified approach for assimilating the various published LPTSPT correlations. Additional SPT and LPT data were collected at a sand research site to check the proposed method and to expand the LPT database. The observed and predicted correlation factors are shown to be in good agreement. The proposed correlation method will be useful as a logical framework for the comparison of LPT data collected throughout the world. Additional research will be required to extend this procedure to gravel sites.Key words: large penetration test, LPT, fundamental correlation method, wave equation analysis, grain size effects.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".