Numerical study on the response of pile groups under lateral loading
Bibliographic record
Abstract
When piles act in a group, soil–pile interaction reduces the lateral resistance of the individual piles. A practical approach to characterize the group behavior in different pile groups is using appropriate factors such as p–multiplier or group reduction factor. The experimental studies on pile groups are usually carried out on small pile groups with close spacings and free-head condition. These limitations are due to the difficulty and high cost of full scale testing particularly in larger pile groups. These limitations justify using three–dimensional numerical simulations to study lateral response of pile groups. This research focuses on group reduction factors and p–multipliers to characterize the group effects in a wide range of pile groups. In order to systematically study the group reduction factors, a numerically derived benchmark database is established using a continuum approach to simulate the response of the pile groups. The capability of the numerical model in predicting the pile group behavior is first evaluated by three–dimensional continuum modeling of three field tests on actual pile groups. Then the continuum model is used to generate benchmark database. The calculated group reduction factors compare well with available experimental data, which are typically extracted from small pile groups. Current study also covers a wide range of pile groups with different numbers of piles, various pile spacings and pile head condition for which there is no experimental data available in the literature. Furthermore, this study gives greater insight into the interaction between piles based on their row position in the pile groups with different layouts. To this end, carried load at the pile head and bending moment profiles for different piles are compared based on their row position in the group when they are pushed simultaneously. The p–multipliers are also calculated to quantify the contribution of different rows to the lateral resistance of the group.The study shows that design guidelines such as AASHTO and FEMA P-751 overestimate the group reduction factors and p–multipliers, hence the lateral resistance, in larger pile groups or pile groups with larger spacings, especially for fixed pile head conditions.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".