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Record W4411809724 · doi:10.11159/ijci.2025.007

Seismic Vulnerability Assessment of Urban Metro Tunnels: Influence of Material Properties and Cover Depth through Numerical Modelling

2025· article· en· W4411809724 on OpenAlexvenueno aff
Pranav Mahajan, BN Rao

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersMinistry of Education, IndiaIndian Institute of Technology Madras
KeywordsVulnerability assessmentCover (algebra)Vulnerability (computing)Geotechnical engineeringGeologyEnvironmental scienceCivil engineeringForensic engineeringEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Structures constructed, either above or below the ground, are susceptible to damage by seismic vulnerabilities.In underground structures, especially tunnels, these vulnerabilities may lead to catastrophic failure causing infrastructural damage and loss of human lives.Therefore, construction of tunnels in urban regions require understanding of complex geological ground conditions for designing the underground tunnel system resistant against vulnerable seismic conditions.Studies indicate concerns in underground tunnels, during and post seismic effect, which require engineering assessment to ensure the tunnel structural safety.Hence, mechanical behaviour analysis of the tunnel lining is essential to determine the influence under varying conditions and its susceptibility to different ground motions.The current study analyses the seismic vulnerability impact on tunnel lining under variability in ground material characteristics and overburden depth.The analysis determines the ground motion impact in both x and y directions to comprehend the tunnel lining behaviour and further improve its seismic resistance.The behaviour is analysed to compare different mechanical parameters required for tunnel lining design in both the directions.To carry out this, three analytical frameworks: linear static, eigenvalue, and nonlinear time history analysis, defines the methodology utilised to simulate the seismic behaviour analysis under different scenarios.This is numerically simulated using finite element software, MIDAS GTS NX, analysing the structural sensitivity against combined variation in material characteristics and overburden depth for different seismic ground motions.The study utilises acceleration time-history plots of the Tokachi and Tohoku Coast ground motions to understand the behaviour.The analysis for different analysed cases suggests significant influence of material characteristics on maximum displacement, axial force, and bending moment compared to the seismic impact.In this, higher tunnel overburden depth leads to higher axial force and bending moment on tunnel lining depicting the influence of overburden depth.Thus, the results obtained using numerical analysis provide comprehensive behaviour of the tunnel lining, which can be utilised by designers for enhancing the seismic resistance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.246
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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