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Record W3107906562 · doi:10.1139/cgj-2020-0538

Dual row retaining walls in dry sand: influence of wall stiffness on seismic response

2020· article· en· W3107906562 on OpenAlexaffvenue
Srikanth S. C. Madabhushi, Stuart K. Haigh

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsRetaining wallGeotechnical engineeringLateral earth pressureServiceability (structure)StiffnessCentrifugeStructural engineeringMechanically stabilized earthConsolidation (business)Shear wallStiffeningOpenSeesBearing capacityEngineeringGeologyFinite element method

Abstract

fetched live from OpenAlex

Dual row retaining walls can form efficient port and embankment structures, or even be used as coastal defence against tsunamis. The system of parallel sheet pile walls can have a large lateral capacity within serviceability limit states due to the combined strength and stiffness of the walls and confined soil. Optimising the design by reducing the wall section and ensuring greater utilisation of the soil capacity has economic and environmental benefits but requires a deeper understanding of the dynamic soil–structure interaction. Centrifuge modelling and numerical modelling are used to elucidate the mechanics of two systems with relatively flexible and stiff walls. Considering the fraction of the wall’s plastic moment capacity mobilised alongside the peak deflections illustrates the advantages of using relatively flexible retaining walls in these systems. More fundamentally, the importance of vertical variations of both the stress and strain during the horizontal dynamic loading is shown. The limiting horizontal stresses and phasing of the stress components around the walls are better understood by considering the mobilisation of earth pressure coefficients, reinforcing previous work that recommends a move away from conventionally defined limiting dynamic earth pressure coefficients.

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)0.001
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 score0.036
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.199
Teacher spread0.188 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207