Concrete Bleeding in Deep Foundations as a Result of Aggregate Grading
Bibliographic record
Abstract
ABSTRACT Concrete bleeding in deep foundation elements like bored piles or diaphragm walls can result in significant defects like channeling or voids in the hardened pile shafts or diaphragm wall panels. The repair of such defects can be time consuming and expensive. Concrete for deep foundations relies on both, optimal workability and stability performance of the fresh concrete during and after placement. It can be challenging to find the required balance in the mix design as the addition of water is necessary for the concrete to achieve optimal workability (and lateral flow characteristics) but at the same time the amount of water must be limited to reduce the risk of bleeding under pressure, which will significantly decrease the fresh concrete workability and potentially cause defect in the hardened concrete. The author introduces a simplified model to simplistically describe the potential and fundamental mechanism of concrete bleeding in deep foundations. The paper will also focus on the effects of aggregate grading and the quantity of fines on the risk of concrete bleeding when external pressure is applied to the fresh concrete. Experimental data is presented which demonstrates that the introduction of fines has significantly reduced the risk of concrete bleeding in selected tremie concrete mixes. Based on such initial experimental data, proposed tremie concrete mixes can be designed to optimise the stability of the fresh concrete which will reduce the risk of bleeding under pressure.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".