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Characterization of Rocking Shallow Foundations on Cohesive Soil Using Field Snap-Back Tests

2019· article· en· W2957052327 on OpenAlexaff
Keshab Sharma, Lijun Deng

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeotechnical engineeringBearing capacityAmplitudeNonlinear systemSettlement (finance)Damping ratioStructural engineeringVibrationMoment (physics)GeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a series of field snap-back experiments of a soil-footing-structure system equipped with rocking foundation on a cohesive soil. The objective was to investigate the nonlinear dynamic behavior of the rocking system subjected to particularly pulse-like motions. During the snap-back tests, an initial drift ratio was applied to the deck, at a maximum of 8.5%, and then released using a quick release mechanism to enable the free vibration of the system. Effects of initial factor of safety against the bearing failure, initial drift amplitude, and snap-back directions on the dynamic properties of the rocking system were investigated. Test results show that the rocking moment versus footing rotation is highly nonlinear and the moment capacity can be well predicted on cohesive soil. The shear capacity of footing did not significantly change with the number of trials or amplitude of initial drift. The damping ratio observed during the oscillations after snap-back release of the shallow foundations ranged from 8% to 30%. The average measured period of the rocking system was elongated by approximately 235% compared with the period of a fixed-base structure. An acceptably small residual settlement was observed even at a high cumulative rotation. The rocking system on clay exhibited a good recentering ability, which is even better than on sand. Finally, the increase in the density and shear strength of soil beneath the footing due to rocking cycles was observed.

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: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.427

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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