Prediction of driven pile resistances in shales considering weathering and time effects
Bibliographic record
Abstract
The difficulty with shale sampling and testing and the lack of pile load tests in shales have created challenges with shale classification, pile resistance prediction, and understanding time-dependent pile responses. Treating shale as soil can yield a conservative pile design. Shales were classified into soil-based and rock-based depending on their weathering conditions, mechanical properties, and measured pile resistances. Failure behaviors of soil- and rock-based shales are discussed. Prediction equations were developed to account for predicting shale properties. Properties of rock-based shales decrease with the increase in weathering. New static analysis (SA) methods were proposed to predict unit shaft resistance ( qs) and unit end bearing ( qb) of piles in shales and validated. Our study yields higher resistance and efficiency factors for the proposed SA methods and piles in shales than existing SA methods developed for piles in soils. The qs in the soil-based shale exhibits pile setup, while the qs in rock-based shales experience both setup and relaxation. The qb in both soil-based and highly weathered shales is likely to experience pile setup. However, the qb in the moderately and slightly weathered shales experience both setup and relaxation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".