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Record W2599704134 · doi:10.1061/9780784480472.049

Numerical Prediction of Stress-Deformation Behavior for a Bridge Approach Embankment on Soft Compressible Clay

2017· article· en· W2599704134 on OpenAlexaff
S. Y. Evan, Andrew J. Whittle

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsConsolidation (business)Geotechnical engineeringLeveeGeologyCompressibilityFinite element methodCritical state soil mechanicsStress (linguistics)Structural engineeringEngineeringConstitutive equation

Abstract

fetched live from OpenAlex

The mechanically stabilized earth (MSE) approach embankments for a new state highway bridge across the Indian River were founded on 8–9 m of dense sand overlying a 20 m thick layer of soft, normally consolidated, high plasticity clay. Consolidation of the clay was accelerated through an array of prefabricated vertical (PV) drains. The instrumented 13.8 m high fill was monitored during staged construction and for a period of 1.25 years after construction. The embankment settled more than 2 m, while large lateral spreading (~0.5 m) was restrained by overlying sand layers. The available site investigation data was carefully reinterpreted and a 2D finite element analyses was performed using rate-independent, effective stress models (Modified Cam-Clay and MIT-E3). The numerical predictions are generally in very good agreement with measured ground settlements and lateral deflections. This study highlights the importance of careful parameter selection in modeling the performance for soft ground construction in order to predict key features such as the asymmetric lateral spreading of the clay during consolidation.

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 score0.945
Threshold uncertainty score0.847

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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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
Published2017
Admission routes1
Has abstractyes

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