Discrete element analysis of a cross-river tunnel under random vibration levels induced by trains operating during the flood season
Bibliographic record
Abstract
Floods result in many problems, which may include damage to cross-river tunnels. The cross-river tunnel, as a new style of transportation, deserves a large amount of attention. In this paper, a large-scale cross-river tunnel model is proposed based on discrete element method (DEM). Micro parameters used in the model are calibrated by proposing a triaxial numerical model. Different in situ strata, high water pressures of normal flood-water levels and random vibration levels induced by running trains are taken into account to evaluate the dynamic characteristics of a high-stress tunnel in deformation and stress analysis. The results show that the upper half of the tunnel, including the concrete lining and the surroundings, is at higher risk than the lower half. Vibration waves transferring into the surroundings undergo an amplification process. The particles of the surroundings at the vault of the tunnel separate and move downward and then reassemble during the dynamic vibrations. The vibration levels, represented by particle accelerations, are lower under flood conditions than those under normal conditions. As train speed increases, the acceleration of the track and particles in the foundation increases, accompanied by a decrease in deformation.
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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 | 0.000 |
| 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".