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Record W4386360080 · doi:10.1139/cgj-2023-0196

Assessment of twin tunnelling induced settlement and load transfer mechanism of a single pile in dry sand

2023· article· en· W4386360080 on OpenAlexvenueno aff
Hu Lü, Jiangwei Shi, Chao Shi, Wei-Wei Pei, Shaoming Chen

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersDepartment of Education of Guangdong ProvinceNanyang Technological UniversityMinistry of Education - Singapore
KeywordsPileGeotechnical engineeringQuantum tunnellingSettlement (finance)Shearing (physics)Parametric statisticsGeologyComputer simulationEngineeringMaterials science

Abstract

fetched live from OpenAlex

This study conducted both three-dimensional physical model tests and numerical back-analyses to examine the performance of a single pile subjected to twin tunnelling beneath the pile toe in dry sand. Moreover, numerical parametric analyses were carried out to assess the impact of working load level and tunnel volume loss on pile behaviour. The findings revealed that the first tunnelling induced a 1.9% d p (pile diameter) settlement in the pile when the pile toe was 0.5 D (tunnel diameter) above the twin tunnels, which had a 1.0% volume loss. The settlement was accompanied by an upward load transfer mechanism. The second tunnelling had a reduced impact and induced only 76% of the settlement caused by the first tunnelling. The study also showed that tunnelling-induced shearing behaviours weakened beneath the pile toe with a decrease in working load, resulting in reduced pile head settlement. Moreover, the computed pile settlement increased by 178% when the tunnel volume loss increased from 0.5% to 2%. The findings of this study offer valuable data for the validation and calibration of numerical models. In addition, the outcome from this study provides useful insights into the prediction of the pile-soil-tunnel interaction when subjected to twin tunnelling.

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)0.001
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 score0.147
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.219
Teacher spread0.204 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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