Soil, Structure, and Motion Factors Impacting Kinematic and Inertial Loading of Pile Foundations
Bibliographic record
Abstract
Pile-supported foundations subjected to large ground deformation and superstructure inertia can deform excessively during strong earthquake shaking. Design guidelines vary on the combination of inertia and kinematics in uncoupled analyses and do not consider the effects of varying soil profiles and structural properties in their recommendations. This study investigates the relative contribution of inertial and kinematic loadings on the overall wharf response during the critical loading cycle as well as the factors that affect this interaction. This was achieved by analyzing experimental data from five large centrifuge tests on pile supported wharves in liquefied soils. Nonlinear dynamic models were calibrated to the centrifuge data and were subjected to different loading scenarios to evaluate the isolated contribution of inertia and kinematic demands. The analysis results provide recommendations for the design of pile-supported wharves subjected to foundation deformation. The similarities and differences of inertial and kinematic interaction between wharf supported by small-diameter flexible piles and bridge foundation supported by stiff shafts with a large-diameter are discussed which provided insight on the applicability of bridge design guidelines to wharf structures.
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 | 0.001 | 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) | 0.001 | 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".