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Record W2589616627 · doi:10.1139/cgj-2015-0313

Failure in anisotropic sensitive clays: finite element study of Perniö failure test

2017· article· en· W2589616627 on OpenAlexvenueno aff
Marco D’Ignazio, Tim Länsivaara, Hans Petter Jostad

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringFailure mechanismLeveeFinite element methodGeologySofteningDirect shear testShear (geology)AnisotropyMaterials scienceStructural engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

The railway network on coastal areas of Finland is predominantly located in soft clay areas. The undrained shear strength of such clays is generally low, highly anisotropic, and rate dependent, and it exhibits post-peak strain softening under undrained conditions. A full-scale failure test was performed by Tampere University of Technology in Perniö, Western Finland, in 2009. A shallow railway embankment built on a soft clay deposit was equipped with a loading structure and loaded to failure in about 30 h. The embankment collapsed 2 h after the last loading step. In this study, data collected from the experiment are used, together with laboratory test results on high-quality samples, to conduct advanced finite element analysis of the Perniö failure test. The NGI-ADPSoft model is used for this purpose, which is capable of simulating the strain-softening behavior of the clay. Even though the observed rate effect is not taken into account in the analyses, the failure load can be predicted reasonably well. Good agreement is also observed for calculated displacements and failure mechanism with experimental observations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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.

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

Citations33
Published2017
Admission routes1
Has abstractyes

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