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Record W4402443639 · doi:10.1139/cgj-2024-0296

Modelling the impact of deterioration on the long-term performance of Dublin Tunnel

2024· article· en· W4402443639 on OpenAlexvenueno aff
Chao Wang, Zhipeng Xiao, Miles Friedman, Zili Li

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversity College DublinScience Foundation IrelandTransport Infrastructure Ireland
KeywordsGeotechnical engineeringTerm (time)Forensic engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

The influence of tunnel deteriorations like hydraulic and mechanical deterioration on its long-term performance has received extensive attention recently. Most studies considered deteriorations by manually varying the magnitude of parameters like permeability and stiffness, often neglecting their time-dependent variation process (individual/coupled). This paper addresses this gap by investigating the impact of time-dependent hydraulic and mechanical deteriorations on the long-term behaviour of the aging Dublin Port Tunnel (DPT). Relevant geotechnical and mechanical properties of ground layers and concrete lining were firstly characterised and determined. A modified analytical relative ground-lining permeability model and calculated deteriorated lining permeability for DPT were presented, with steps and procedures generalised. The deteriorated permeability of DPT was incorporated into the hydraulic deterioration model thereafter, together with tunnel mechanical deterioration, offering a more holistic and realistic prediction of DPT’s deterioration-induced long-term performance than previously available. Numerical results, compared against field measurements, showed that (1) assuming constant tunnel permeability during its lifetime fails to accurately capture time-dependent liner deformation, and hydraulic deterioration has been identified as the dominant factor inducing an approaching squatting deformation mode, which can be attributed to twin tunnel interaction effect; (2) continuous mechanical deterioration leads to a linear growth in both vertical and horizontal convergence over time, with vertical convergence being more pronounced, indicating a squatting contraction deformation mode that could be associated with reduced ability to support ground pressure and external loads; and (3) the comparison quantitatively evaluates the impact of individual and coupled hydro-mechanical deterioration on DPT’s long-term behaviour and the agreement between field data and numerical results confirms that coupled lining deterioration is the root cause behind the monitored lining deformation.

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 score0.079
Threshold uncertainty score0.312

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 integrity0.0000.001
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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