Load Distribution in Large Pile Groups for Static and Seismic Loading
Bibliographic record
Abstract
A large number of modern structures are founded on piles and often these foundations contain large pile groups. In contrast, most of the research on the action of pile groups in resisting lateral loading is based on analysis and centrifuge tests of small pile groups, mostly 3x3 pile groups. The pile-soil-pile interaction in these groups is modelled by modifying the p-y curves or by using a group factor for the entire group to simulate the same effect. The values for changing the p-y curves and the appropriate group factors are based entirely on static tests and there is no direct verification that these factors are appropriate to handle pile groups under seismic loading. In this paper we investigate the interaction effect between piles under static and seismic loading using the computer program VERSAT-P3D (Wu 2006), which has an equivalent linear constitutive model for the soil. The procedure adopted is to calibrate the model for the static experimental tests of 3×3 pile groups by Christensen (2006). Then we expanded the pile group sizes to 3×3, 5×5, 8×8, 10×10, 10×2 and 15×2 pile groups. The distributions of shear forces in the piles at the pile cap level were developed for these pile groups under both static and seismic loading and the distribution of static and dynamic shear forces at various lateral displacements were evaluated. This study is ongoing and a sample of the results will be discussed in this paper. The soil model was calibrated to replicate the experimental results obtained by Christensen (2006). The pile properties used were taken from Christensen’s single pile test directly. It is demonstrated that VERSAT-P3D can replicate closely the experimental results of Christensen. Of the preliminary results from this study, those which will be of primary interest to the engineering community will be those that show how the distribution of load within a pile group under static loading varies from the distribution under a dynamic loading scenario. This load distribution within a row also varies with the location of each row within the pile group, from the center of the group to the outside edge. Also, the distribution varies with the intensity of shaking but in a manner different to the variation with increasing static load. These preliminary results show that using static load distribution factors needs to be reconsidered. When the study is completed a clearer picture of the implications of relative load distributions should emerge.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".