Effect of spatial correlation of standard penetration test (SPT) data on bearing capacity of driven piles in sand
Bibliographic record
Abstract
In this paper, the effect of spatial correlation of standard penetration test (SPT) data on the bearing capacity of driven piles in sand is analyzed. First, the direct approach for using SPT data to determine the bearing capacity of piles in sand is used to derive the expressions for probabilistic prediction of pile bearing capacity by considering the spatial correlation of the SPT data. To analyze the relationship between the probability of failure and the factor of safety, a procedure based on the advanced first-order, second-moment (FOSM) method is used. Then parametric studies are conducted on the spatial correlation between the spatial average of SPT numbers over the pile length, NLV, and the spatial average of SPT numbers over an interval near the pile base, NbV, and its effect on the bearing capacity of piles. The results indicate that it is important to consider the spatial correlation between NLVand NbVin the probabilistic prediction of pile bearing capacity. Ignoring this spatial correlation will underestimate the probability of failure and lead to unsafe design. Finally, three tested piles are analyzed to demonstrate the probabilistic analysis of piles by considering the spatial correlation of SPT data and the procedure for probabilistic analysis of pile bearing capacity is summarized.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".