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Record W2774749788 · doi:10.1139/cgj-2017-0093

Centrifuge modeling of geotechnical mitigation measures for shallow foundations subjected to reverse faulting

2017· article· en· W2774749788 on OpenAlexvenueno aff
Mehdi Ashtiani, Abbas Ghalandarzadeh, Mehdi Mahdavi, Majid Hedayati

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsTrenchCentrifugeGeotechnical engineeringGeologyFoundation (evidence)EmbedmentFault (geology)Shallow foundationSettlement (finance)SeismologyBearing capacity

Abstract

fetched live from OpenAlex

Surface fault ruptures are particularly damaging to buildings, lifelines, and bridges located across or adjacent to active faults. These structures should be designed in consideration of surface fault rupture hazards or strategies should be adopted to protect the structures from fault-induced damage. Geotechnical mitigation strategies such as diversion of the fault rupture away from the structure and diffusion of the rupture over a wide zone are possible strategies. The effectiveness of these geotechnical mitigation measures for reverse faulting on shallow embedded foundations was investigated using a series of centrifuge tests. These measures included excavation of a vertical trench adjacent to the foundation and installation of geogrid layers beneath the foundation. The trench was shown to be effective for a range of foundation positions depending on the magnitude of the fault offset, dip angle of the fault, depth of the trench, embedment depth of the foundation, and the number of trenches used. The geogrid layers prevented a distinct fault rupture from reaching the surface and spread fault displacement over a wider zone, but were unable to mitigate the surface fault rupture hazard for shallow embedded foundations.

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)0.003
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: none
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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