Load transfer and performance of an improved grouted enlarged toe (GET) precast concrete pile
Bibliographic record
Abstract
An improved grouted enlarged toe (GET) precast pile is proposed in this study. The GET technology improves the bearing capacity of precast concrete piles by enlarging the pile toe and solidifying soil surrounding pile shaft. In this study, the soil densification due to the GET technology is first investigated by developing an analytical solution for the soil deformation around the pile shaft, in which the pile toe expansion is generalized as an oblate spheroid. The results of this solution show that the GET pile can alleviate ground surface deformation. Secondly, a multi-layer load transfer (MLT) method is proposed to describe the pile-solidified soil–ground interaction, which allows assessing the performance of the GET pile. In addition, full-scale conventional precast reinforced concrete pipe and GET piles were constructed and load tested at two field sites. The test results demonstrate that the bearing capacity of GET piles is significantly improved compared to the conventional pipe piles, with an increase ranging from 36% to over 60% under varying site and construction conditions. Finally, the load–settlement curves obtained from the proposed MLT method were in good agreement with the results obtained from the static load tests, which confirmed the ability of the MLT method to reliably predict the pile performance under axial loading.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".