Prediction of the horizontal load-displacement curves of pile groups based on the results of single pile tests
Bibliographic record
Abstract
This paper presents the prediction of horizontal load-displacement curves of pile groups based on the results of single pile tests. Although the same basic model is employed, two different approaches are taken: one assumes soil to be linear elastic-plastic, and the other assumes it to be elastic nonlinear. The model is calibrated on the basis of the results of a full-scale single pile test. Special emphasis is placed on model calibrations, since the success of any prediction method depends on a careful characterization of the soil. Some new approaches for determining the soil parameters are presented. Two methods for predicting load-displacement curves, one from each model approach, are then proposed and discussed. Special emphasis is placed on group efficiency in the elastic-plastic method and on the boundary conditions of the single pile and the pile group in the elastic nonlinear method. Using the soil characteristics from the model calibrations, the load-displacement curves for a given pile group are then predicted. These predictions are compared with the results of a full-scale pile group test carried out at the same site as that of the single pile test. Agreement between the predictions and the test results tends to validate the methods proposed.Key words: displacement predictions, pile groups, model calibration, pile tests.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".