Understanding Material Characteristics and Cover Depth Impact on Urban Metro Tunnels under Seismic Vulnerability: A Numerical Study
Bibliographic record
Abstract
The behavior of underground tunnels in urban regions requires careful consideration of the complex underground conditions and involves designing the underground tunnel system in vulnerable seismic conditions.Studies have indicated consequences in underground tunnels during and after ground excitation, which needs engineering assessment to ensure structural safety.In the current study, the impact of seismic vulnerability is analysed for site condition variability and overburden depth under different ground motion impacts in x and y directions to understand the tunnel stability and improve seismic resistance.Analysis of seismic vulnerabilities is carried out using three analytical frameworks, which includes linear static, eigenvalue, and nonlinear time history analysis.Simulation of the tunnel behavior under such conditions is carried out using finite element software, MIDAS GTS NX for determining the structural sensitivity to material characteristics variation and overburden pressure for different earthquakes.Acceleration time history analysis of Tokachi and Tohoku Coast earthquakes is used to determine the behavior.The behavior suggested that the maximum settlement, axial force, and bending moment have a significant influence on material characteristics compared to the seismic impact.Increase in tunnel overburden depth also leads to higher axial force and bending moment, which is also influenced by the seismic ground motion observed.These outputs provide comprehension of the tunnel behavior under different materials and overburden depth subjected to different ground motion, which can be utilized for designing the seismic isolators provided between tunnel lining and surrounding soil.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".