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Record W2807130091 · doi:10.11159/iccste18.129

Evaluation of Seismic Performance of Earth Dams Due to the Level of Its Reservoir Using Finite Element Method

2018· article· en· W2807130091 on OpenAlexvenueno aff
Amin Didari, Mohammad Hassan Saddagh

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodEarth (classical element)GeologyComputer scienceStructural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

One of the challenges in engineering world is investigation on seismic behavior of earthdams during an earthquake. The complexity of this issue is lack of adequate information about the impact of the earthquake on earthdams behavior. Many scientists presented an important research in this area. Dam stability during the earthquake is one of considerable factors which can be estimated by various parameters. In this paper, as a reliable parameter, safety factor is estimated using finite element method in PLAXIS 2D software. In this paper, reservoir water level is considered as a noticeable parameter which affects on stability of earthdam and safety factor during an earthquake. For this purpose, two different water levels, %30 and %70 of earthdam height, is assumed for reservoir. Also, in order to consider effects of earthquake, a magnitude MW 6.8 earthquake is applied to model. Angle of internal friction of earthdam materials is 30 to 35 Degree and the side slopes are 1:2.7. Additionally, as isotropy has no enormous impact on results, isotropic materials are assumed for earthdam. Analysis results indicate reservoir water level increasing from %30 to %70 of earthdam height has direct impact on safety factor reduction up to %8.

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 score0.138
Threshold uncertainty score0.344

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 head0.066
GPT teacher head0.294
Teacher spread0.227 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicDam Engineering and SafetyFrench-language works237,207