An existing pile efficiency method for the design of lateral new pile behavior in sites with existing piles
Bibliographic record
Abstract
Foundation reuse is the development trend of sustainable engineering construction. However, the reuse of existing piles as rigid inclusions and their contribution to newly constructed piles (new piles) under different new and existing pile spacings are still insufficiently understood. In this paper, a series of numerical investigations were conducted based on centrifuge tests to examine the effect of existing piles on the lateral behavior of new piles under different pile spacings. The existing pile efficiency that reflects the contribution of existing piles to the lateral capacity of new piles was analyzed, and it was shown that the impact of existing piles decays exponentially with the pile spacing between new and existing piles. Beyond the spacing of 6 D ( D: the diameter of new piles), the lateral new pile–soil–existing pile interaction can be neglected. A parametric study was subsequently performed to quantify the existing pile efficiency under different variables, including the new pile-head restraint, loading displacement, bending stiffness, load eccentricity, embedded depth, and soil properties. Finally, an existing pile efficiency-based method for the estimation of the lateral capacity of new piles was proposed, which provides an efficient way to design laterally loaded new piles in sites with existing piles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".