Numerical simulation of installation of jacked piles in sand using material point method
Bibliographic record
Abstract
Pile installation has a great impact on the subsequent mechanical pile response. It is not, however, routinely incorporated in the numerical analyses of deep foundations in sand. Some of the difficulties associated with the simulation of the installation process are related to the fact that large deformations and distortions will eventually appear. The finite element method is not well suited to solve problems of this nature. Hence, an alternative procedure is tested herein, by using the material point method to simulate the installation of statically jacked or pushed-in type piles, which has successfully demonstrated its capacity to deal with this simulation. Two constitutive models were also tested, i.e., the modified Cam clay (MCC) and the subloading Cam clay (SubCam), allowing a clear perception of the great advantage to consider the soil with the SubCam model. The simulations have indeed reproduced some of the important features of the pile installation process, such as the radial stress acting around the pile’s shaft or the shaft’s lateral capacity, among other issues. The numerical results were additionally compared with known (semi-empirical) methods to derive the lateral capacity of the shaft, with a good and practical outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".