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Record W4392683419 · doi:10.1139/cgj-2022-0653

Evaluation of deformation for two-dimensional (2D) and three-dimensional (3D) braced excavation in clays with centrifuge modelling and numerical analysis

2024· article· en· W4392683419 on OpenAlexvenueno aff
Xianfeng Ma, Ming Cao

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCentrifugeGeotechnical engineeringExcavationGeologyDeformation (meteorology)Engineering

Abstract

fetched live from OpenAlex

This study presents an improved in-flight strutting system for centrifuge modelling. Based on the centrifuge model test data, the mobilizable strength design (MSD) method was revised. The modified MSD method extends to the prediction of three-dimensional (3D) deformations in retaining walls due to excavation activities. Initially, an improved in-flight excavation tool used in centrifuge tests was employed to investigate the impacts of staged excavation on the characteristics of wall displacement and ground settlement in two-dimensional (2D) braced excavation. Subsequently, 2D and 3D finite element analyses, calibrated against the centrifuge test data and considering the small strain effect, were conducted to assess the performance of wall displacement, ground settlement, and movement of the underground soil induced by the braced excavation. Moreover, this research proposes a straightforward predictive model based on the modified MSD, specifically for calculating the deformation along the length of retaining walls in 3D braced excavations. The insights and the modified MSD method given in this study may facilitate the design of foundation pit projects, especially when the 3D effects are an essential detail to be considered in these projects.

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: none
Teacher disagreement score0.638
Threshold uncertainty score0.556

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 integrity0.0000.000
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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