New ultimate pile capacity prediction method based on cone penetration test (CPT)
Bibliographic record
Abstract
Our research evaluated current cone penetration test (CPT) pile design methodologies (Schmertmann, Laboratoire Central des Ponts et Chaussées (LCPC), etc.), modified one of these methods (Philipponnat method), and proposed a new one to improve future driven pile designs. This was accomplished by evaluating 14 pile-capacity-design methods based on CPT data. Furthermore, load and resistance factor design (LRFD) resistance factors for each method were calculated using 21 cases from Florida and 28 from Louisiana. The resulting resistance factors were not satisfactory for any of these methods. A new design method — the University of Florida (UF) method — was proposed taking into account cementation, soil type, and historical data. The LRFD resistance factor was also assessed for this new method. The proposed UF method provides better LRFD resistance factors for both Florida and Louisiana soils. It could be a promising alternative for improving pile design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".