NUMERICAL ANALYSES FOR THE SEISMIC SAFETY RETROFIT
Bibliographic record
Abstract
Dynamic numerical analyses were used for the seismic retrofit design of the 44-year-old immersed-tube George Massey Tunnel. The 1.3 km long tunnel carries four lanes of traffic under the Fraser River just south of Vancouver, British Columbia. Design criteria were that the retrofitted tunnel should withstand both a 0.25g magnitude 7.0 non-subduction earthquake and a 0.15g magnitude 8.2 distant subduction earthquake without collapse or loss of life, but with damage to a repairable level including controllable water leakage. Soil liquefaction, its consequences, and mitigation were the key design challenges. Two-dimensional dynamic analyses using the program FLAC were the prime geotechnical analyses and design tool. Displacements from the numerical analyses were used as input into three-dimensional static structural analyses using non-linear soil springs and nonlinear moment-curvature section properties. The structural analyses were used to assess and mitigate potential cracking in the tunnel. In the 2D FLAC analyses transverse and longitudinal sections were studied using total and effective stress constitutive models (UBCTOT and UBCSAND) developed at the University of British Columbia. Dynamic shaking, liquefaction triggering, consequences of liquefaction and soil-structure interaction were addressed in each of the models. Analyses were carried out with and without retrofit measures. A
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".