Co-seismic stress changes and damage to tunnels in the 23 October 2004 Mid-Niigata Prefecture earthquake
Bibliographic record
Abstract
Recent near-field earthquakes accompanied by large soil deformations have been clouding the notion that mountain tunnels would be safe places during earthquakes. One of the recent eye-openers was the damage to railway tunnels in the 2004 Mid-Niigata Prefecture earthquake. Changes in stresses in the interior of a half-space of stratified sedimentary rocks, as a representative of the earthquake-hit region, are obtained using the authors’ previous works on co-seismic deformations of this region to study the damage mechanism of deeply embedded railway tunnels. The values of square root of the second invariant of the stress deviator tensor, [Formula: see text], and the first invariant of Cauchy stress tensor, [Formula: see text], are compared with the reported damages along the entire stretch of selected tunnels and a very good correlation is observed between the peak values of [Formula: see text] and the damaged sections of the tunnels. A yield surface is defined as the boundary between clusters of points for damaged and undamaged tunnel sections in the scatter diagram of [Formula: see text] and [Formula: see text]. This yield surface and rock–soil deformability can be used to examine the margin of safety of both existing and new tunnels as well as for hazard zonation in a given scenario earthquake.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".