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Record W4392618834 · doi:10.1139/cgj-2023-0227

Observed soil arching-induced ground deformation and stress redistribution behind braced excavation

2024· article· en· W4392618834 on OpenAlexvenueno aff
Muchun Liu, Fan‐Yan Meng, Zhen Liu, Renpeng Chen

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersHunan UniversityNational Natural Science Foundation of China
KeywordsGeotechnical engineeringExcavationGeologyJackingRetaining wallDeformation (meteorology)Stress (linguistics)Redistribution (election)Engineering

Abstract

fetched live from OpenAlex

This paper presents observed arching-induced ground deformation and stress redistribution behind braced excavation using the top-down construction method. The soil properties around the excavation were determined by laboratory and field tests. The ground deformation, soil displacement vector, strain path, principal strain, maximum shear strain, lateral earth pressure, pore water pressure, and effective stress path are presented based on the measured data. The majority of soil behind the wall is under volumetric expansion, indicating consolidation, creep behavior, or a combination of both. Besides, two periods of increases in pore pressure are observed, due to stress transfer from the lower to the upper parts (i.e., soil arching effect). The deep inward movement of the wall and the nearby soil accounts for the distribution of lateral earth pressure acting on the wall. The soil located behind the area of maximum wall deformation and adjacent to the wall, as well as the soil below the excavation base intersected by the shear plane, is in an active stress state. The lateral earth pressure at 5 m from the left excavation wall showed minimal changes, due to the combined effects of soil arching from lateral excavation and shield tunneling.

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 score0.147
Threshold uncertainty score0.807

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

Explore more

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