EPB machine excavation of mixed soils – Laboratory characterisation
Bibliographic record
Abstract
Abstract Earth pressure balance (EPB) tunnel boring machines are shield machines that rely on their own excavated material as a support medium to maintain the support pressure at the face. This material also needs to have the necessary properties to be extracted, transported and, finally, disposed of. Whenever the natural material does not fulfil the necessary requirements, additives like water, foam, polymers, and fines, must be added, modifying the excavated ground to the desired conditions. The rheological properties of any excavated material, together with any additives, must be investigated and understood, as they will influence the flow behaviour of this conditioned material, directly affecting the machine operation and tunnel logistics. While studies assessing the flowability related to the EPB excavation of sand or clay soils are available, there is a lack of information on mixed soils. This paper presents the results from a testing campaign with mixed clay‐sand samples, aiming to reproduce a simplified tropical weathered mixed soil, investigating its flow behaviour when changing certain controlled variables: clay‐sand proportions, clay mineral, size of the clastic grain mixed with clay, water content, and additives (foam and polymers). Results from the tests conducted with a flow table, a slump test, and a rheometer device were compared, providing insights about the flow behaviour of the tested samples and its interaction with an EPB machine.
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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) | 0.000 | 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 | 0.001 | 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".