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Record W2334226374 · doi:10.1061/40794(179)140

Feasibility of Modelling of Soil-Structure Interaction Problems Using Full Three-Dimensional Finite Element Method

2005· article· en· W2334226374 on OpenAlexaff
H. C. Yeow, Richard E. Prust

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsFinite element methodComputationComputer scienceSpare partLinear elasticityCivil engineeringEngineeringIndustrial engineeringStructural engineeringMechanical engineeringAlgorithm

Abstract

fetched live from OpenAlex

Geotechnical problems have become more complex as we utilise every spare inch of the ground in urban environment. We need to improve our understanding of the ground behaviour in any underground works and to have more comprehensive, but accessible, techniques to analyse these problems. Ground movement prediction has become more important in such urban development and the need to incorporate advanced non-linear small strain constitutive model in complex soil-structure modelling has become the norm of the industry. Designers are now familiar with complex two-dimensional (2D) finite element (FE) analyses with highly complex soil models. However, three-dimensional (3D) analyses involving soil-structure interaction problems have been limited to large projects or projects involving fund-rich clients because of the high cost and long computation time involved in undertaking such analyses. Others have attempted such analyses by making many simplifications in order to minimise the size of the problems being investigated or even utilising technique involving the use of multi-processors in their computation. This paper presents some examples where full 3D modelling undertaken using a standard PC was used to solve routine geotechnical problems using a state-of-the-art non-linear small strain constitutive model.

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: none
Teacher disagreement score0.534
Threshold uncertainty score0.449

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 head0.039
GPT teacher head0.267
Teacher spread0.228 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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