Towards Implementing SCPTu Geotechnical Design Guidelines for the State of Illinois
Bibliographic record
Abstract
The cone penetration test (CPT) is widely used in geotechnical engineering for subsurface soil characterization due to its nearly continuous profiling, expediency, and repeatability, which are difficult to match with drilling, sampling, and laboratory testing, or other subsurface characterization techniques such as the standard penetration testing (SPT). In addition to obtaining the cone tip resistance, sleeve friction, and pore water pressure, the seismic piezocone penetration test (SCPTu) also provides measurements of shear wave velocities with depth. A series of SCPTu soundings have been completed at several strategic Illinois test locations to characterize the particular response of Illinois soils in terms of stress history, strength, compressibility, stiffness, organic content, and hydraulic properties. Additionally, since the current geotechnical engineering practice in Illinois has a heavy reliance upon the SPT, an evaluation of correlations between paired sets of SCPTu readings and SPT blow counts corrected for energy efficiencies was made at a selected location. The findings of this research will be incorporated into the Illinois Department of Transportation’s (IDOT) geotechnical manual through the development of guidelines for using CPT in the state of Illinois. This effort is geared towards expanding the use of CPT in IDOT practice allowing for higher quality subsurface data that can reduce design costs while increasing sustainability and reducing risk.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".