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Record W2316335459 · doi:10.1139/t11-107

Seismic deformation analysis of Tuttle Creek Dam

2012· article· en· W2316335459 on OpenAlexaffvenue
Timothy D. Stark, Michael Beaty, Peter Byrne, Gonzalo Castro, Francke C. Walberg, Vlad Perlea, Paul J. Axtell, John C. Dillon, William B. Empson, David L. Mathews

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeotechnical engineeringConstitutive equationGeologyDeformation (meteorology)Settlement (finance)CrestFoundation (evidence)EngineeringFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

To facilitate the design of seismic remediation for Tuttle Creek Dam in east central Kansas, a seismic finite difference analysis of the dam was performed using the software FLAC and the UBCSAND and UBCTOT soil constitutive models. The FLAC software has a key advantage because it can use calibrated site-specific constitutive models. Earlier deformation analyses using a hyperbolic constitutive model for the foundation fine-grained materials did not properly represent the modulus and strength reduction and predicted extremely large permanent deformations. Cyclic triaxial laboratory tests using high-quality samples and in situ vane shear tests were used to calibrate the FLAC constitutive model herein. The resulting FLAC analysis of the unremediated dam predicted an upstream slope toe deformation of about 0.6 m, a crest settlement of about 0.6 m, and a downstream slope toe deformation of about 1.5 m using the design ground motion. Based on the estimated permanent deformations and other factors, it was decided that the anticipated upstream slope and crest deformations were tolerable and only the downstream slope had to be remediated to protect the downstream seepage control system.

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 score0.255
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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