Laterally Loaded Pile Behaviors in Clay with Uncertainty in Strain <i>ɛ</i> <sub>50</sub>
Bibliographic record
Abstract
Uncertainty in soil strengths and lateral loads have been the main foci of the probabilistic analyses of laterally loaded piles in clay. Very little attention has been paid to the uncertainty in strain ɛ50, while it apparently governs the slope of p-y curves and affects the lateral behaviors of piles in clay. This study develops a probability model for strain ɛ50 based on a recently published database, and conducts a probabilistic analysis for a pile design case. The strain ɛ50 was taken as the single random variable that is independent of undrained shear strength. A spreadsheet was developed to perform extensive calculations from the Monte Carlo approach in the probabilistic lateral pile analyses. The probabilistic analysis results demonstrated that the variability in strain ɛ50 has substantial impacts on the lateral displacements and the pile stresses. The uncertainty in strain ɛ50 should be carefully considered in the laterally loaded pile analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".