A novel theoretical model of heterogeneous soil-pile interaction for investigating the torsionally loaded pile
Bibliographic record
Abstract
Abstract A new theoretical model of heterogeneous soil-pile interaction (HSPI) in torsion is developed in this study to analyze the behavior of an end-bearing pile under torque. By assuming the surrounding soil as an elastic continuum following a power law variation in shear modulus depth, new governing equations are established for the torsional deformation of the continuous heterogeneous soil and pile system subjected to torque. Separation of variables and principles of Sturm-Liouville theory are applied to solving the governing equations of the pile and soil, taking into account the interaction conditions between the pile and soil. Rigorous solutions are obtained for the twist angle and internal torque along the pile shaft, as well as the shear stress with depth provided by the surrounding soil. Validation of the present HSPI model by comparing with previous theoretical and experimental data indicates the applicability and superiority of the derived solutions. The HSPI model is subsequently applied to conduct extensive arithmetic examples for investigating the torsional response of piles accounting for complex soil heterogeneity patterns and pile configurations. The obtained results can contribute to a deeper understanding of the operational mechanics involved in torsionally loaded piles in heterogeneous soil.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".