Dynamic Axial Response of Tapered Piles Including Material Damping
Bibliographic record
Abstract
Tapered piles are used in place of cylindrical piles for their improved geotechnical performance. When tapered piles are subjected to axial dynamic loading, as is the case with machine foundations, an appropriate theoretical formulation must be used to obtain an accurate representation of the dynamic response. Material damping, which is the energy loss due to particle deformations, friction, and heat, is often neglected in analyses for simplicity. However, previous researchers have not quantified the implications of neglecting material damping during the dynamic design of tapered piles. The present study uses advanced symbolic computation techniques to introduce material damping within the existing theoretical formulations reported in the literature. Numerous hypothetical case studies are defined to represent a range of realistic site conditions, and the influence of material damping on the dynamic axial response is assessed. It is shown that in all cases, material damping acts to reduce the resonant amplitude and increase the resonant frequency, with the shift in resonant peak being more pronounced for piles founded in soft soils. Provided that adequate dynamic site data are available, material damping should be incorporated within the dynamic axial analyses of tapered piles to produce a more comprehensive representation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".