MPM Simulation of the Installation of an Impact-Driven Pile in Dry Sand and Subsequent Axial Bearing Capacity
Bibliographic record
Abstract
Pile installation leads to significant changes in soil state (i.e., void ratio and effective stress) around the pile, which affects stiffness and bearing capacity. Currently, the driveability of piles is analyzed using empirical methods, and the ultimate bearing capacity is estimated without considering the installation effects. This paper presents simulations of the entire installation and subsequent axial bearing capacity of a close-ended pile using a single numerical tool based on the material point method (MPM). A lab-scale experiment is used as a validation case, where the pile is first impact-driven in dry sand, with different initial relative densities (from loose to very dense), and then axially loaded. A state-dependent constitutive model (DeltaSand) is used in the numerical simulations to predict the mechanical behavior of the sand at different relative densities with a single set of input parameters. The paper also illustrates several enhancements needed to obtain more accurate results: (1) an improved contact algorithm that allows gap closure; (2) a rigid-body formulation for the pile body; and (3) a general analytical solution for calculation of energy-consistent impact forces in uncoupled hammer-pile systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".