Influence of laterally loaded sleeved piles and pile groups on slope stability
Bibliographic record
Abstract
Many high-rise buildings, bridges, and transmission towers are constructed on steep slopes in Hong Kong and are supported by large-diameter piles. These structures may be subjected to large lateral loads, such as those caused by typhoons, earthquakes, and high-speed vehicles. The margin of safety of the slope may decrease as a result of stresses transferred from the piles to the slope. To minimize the transfer of lateral load from the buildings to the shallow depths of the slope, an annulus of compressible material (sleeving) is sometimes formed between the piles and the adjacent soils. In this paper, a three-dimensional analysis is carried out to investigate the effects of unsleeved and sleeved single piles and pile groups on the stability of a cut slope. Mechanisms of load transfer from the piles to the slope are studied. The stability of the slope is evaluated using the strength reduction technique. The evolution of slope failure is examined and the factors of safety for both initiation of instability and global failure of the slope are identified from the numerical analyses. The sleeving technique is found to be capable of significantly reducing the stresses in the shallow depths of the slope in front of the piles, thus improving the local stability of the slope, but offers limited benefit with respect to global stability.Key words: laterally loaded pile and pile group, sleeving, slope stability, three-dimensional analysis, load transfer mechanism, factor of safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".