Anti-seismic Effect of the Shock Absorption Layer in Tunnel
Bibliographic record
Abstract
Tunnels play a vital role in the transportation industry and many other industries.In fact, a tunnel damaged by an earthquake has a serious impact on traffic.Therefore, tunnel destruction induced by earthquakes have been paid more and more attentions.In this study, vibration theory is used for a simplified mechanical model of a tunnel lining system composed of a primary lining, a layer of shock absorption and a second lining.The parameters, such as the mass ratio, the damping ratio and the stiffness ratio of the vibration equation, are simplified correspondingly and solved in the complex domain, resulting in a simplified transfer coefficient.At the same time, numerical simulation and analysis are also carried out.The results show that: 1) The shock absorption layer with a low stiffness ratio has a good damping effect under low-frequency vibration, while under high-frequency vibration, the stiffness ratio has less influence on the transmission coefficient.2) By analyzing the results of the theoretical calculation and numerical simulation, the tunnel structure under high-frequency vibration can be seen, and when the shock absorption layer stiffness is in close proximity to the second lining stiffness, the deformation and stress of the tunnel lining are the smallest.In general, the application of double-lining in strong earthquake zones can reduce the vibration of the tunnel lining and protect the tunnel from damage.
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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 | 0.000 | 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".