Settlement Prediction for Cast-in-Place Tubular Piles with Large Diameters Based on the Load Transfer Approach
Bibliographic record
Abstract
Large-diameter cast-in-place tubular piles offer high efficiency and adaptability for various engineering applications. Despite their widespread use, the bearing behavior of these piles remains complex due to the interactions with the internal soil core, and the related theoretical framework is not yet fully developed. In this study, a simplified load transfer model is proposed based on the pile–soil interaction mechanism of large-diameter tubular piles. Comprehensive load transfer models for the skin friction and end resistance of both the pile body and the soil core are established, supported by a detailed theoretical analysis. A novel three-criteria approach is introduced for the first time to enhance settlement predictions for large-diameter tubular piles by considering the displacement coordination mechanism of the internal soil core, addressing the limitations of traditional two-criteria methods. The proposed methods are validated through two engineering case studies, demonstrating their effectiveness and confirming their rationality and applicability in practical scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".