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Record W7115731493 · doi:10.71846/18-wcee-2666

SEM TUNNEL SEISMIC DESIGN USING ITERATIVE SSI ANALYSIS FOR HIGH SEISMICITY REGIONS

2025· article· en· W7115731493 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationInduced seismicitySeismic analysisIterative and incremental developmentIterative methodSeismic loadingEngineering design processEarthquake engineeringDesign methods

Abstract

fetched live from OpenAlex

This paper presents a state-of-the-art review of the non-linear seismic design philosophy adopted for two tunnels to be excavated parallel to each other with Sequential Excavation Method (SEM) in Vancouver, Canada, known as an active seismic region. The two parallel tunnels were elliptical shaped with a maximum excavation height of approximately 5.6 m and width of 6.1 m, located at the transition between soft ground and bedrock. The seismic performance requirements included minimal damage and repairable damage for 100-year and 2,475-year return period earthquakes, respectively. An iterative procedure is presented using numerical soil-structure interaction (SSI) analysis where the tunnel is designed to withstand the ovaling/racking, and differential deformations imposed by the ground. In addition, the methodology described in this paper allows the iteration between the unfactored non-linear geotechnical FE analysis and the factored non-linear structural FE analysis. Although LRFD (Load and Resistance Factor Design) structural design contrasts the unfactored geotechnical SSI analysis, iterative process and tailored algorithm allows to maintain the design philosophy of both worlds without the need to compromise underlying physics. This methodology can be used in seismic design of underground structures where different load factor should be applied to different loads such as vertical ground pressures, horizontal ground pressures, groundwater pressure, seismic and etc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.228
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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