Case Study: Correlation between Becker and SPT Blow Counts
Bibliographic record
Abstract
This paper presents the results of a site-specific study correlating Becker penetration test blow counts to that of the standard penetration test. The study was conducted as part of the Woodfibre LNG export facility project. The project site is located near Squamish, British Columbia. Site investigation for the project included standard penetration tests, Becker penetration tests, and instrumented Becker penetration tests. The blow count values from the three different test methods needed to be converted to equivalent standard penetration test values for use in seismic analyses and design of the various LNG infrastructure types. Four different methods and correlations were evaluated. Comparison of the equivalent blow-count data obtained from adjacent boreholes with different test methods shows wide differences in the resultant equivalent standard penetration test values obtained using currently available conversions methods. We propose site-specific conversion factor to correlate blow-count data from the different test methods for sand and gravel soil types. This paper also presents a framework to correlate the bounce chamber pressure and hammer energy measurements, which can be used to convert the blow counts from Becker penetration tests to equivalent standard penetration test values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".