Dynamic analysis of laterally loaded pile groups in sand and clay
Bibliographic record
Abstract
Pile foundations supporting bridge piers, offshore platforms, and marine structures are required to resist not only static loading but also lateral dynamic loading. The static py curves are widely used to relate pile deflections to nonlinear soil reactions. The p-multiplier concept is used to account for the group effect by relating the load transfer curves of a pile in a group to the load transfer curves of a single pile. Some studies have examined the validity of the p-multiplier concept for the static and cyclic loading cases. However, the concept of the p-multiplier has not yet been considered for the dynamic loading case, and hence it is undertaken in the current study. An analysis of the dynamic lateral response of pile groups is described. The proposed analysis incorporates the static py curve approach and the plane strain assumptions to represent the soil reactions within the framework of a Winkler model. The model accounts for the nonlinear behaviour of the soil, the energy dissipation through the soil, and the pile group effect. The model was validated by analyzing the response of pile groups subjected to lateral Statnamic loading and comparing the results with field measured values. An intensive parametric study was performed employing the proposed analysis, and the results were used to establish dynamic soil reactions for single piles and pile groups for different types of sand and clay under harmonic loading with varying frequencies applied at the pile head. "Dynamic" p-multipliers were established to relate the dynamic load transfer curves of a pile in a group to the dynamic load transfer curves for a single pile. The dynamic p-multipliers were found to vary with the spacing between piles, soil type, peak amplitude of loading, and the angle between the line connecting any two piles and the direction of loading. The study indicated the effect of pile material and geometry, pile installation method, and pile head conditions on the p-multipliers. The calculated p-multipliers compared well with p-multipliers back-calculated from full scale field tests.Key words: lateral, transient loading, nonlinear, pilesoilpile interaction, py curves, Statnamic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".