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Record W2221422639

Temperature Distribution Analysis in Double-Arced Concrete Dams under Environmental Reactions- A Case Study of the Dez Hydro-Electrical Dam in Northern Dezful

2013· article· en· W2221422639 on OpenAlexvenueno aff
Siamak.Ahmadi Fard, Amin Bagheban Zade Dezfouli

Bibliographic record

VenueJournal of academic and applied studies · 2013
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceAridGeotechnical engineeringGeology
DOInot available

Abstract

fetched live from OpenAlex

Variations in temperature and their resulting stress and strain on concrete dams is of vital importance because of their effects on sustainability and operational efficiency of these hydraulic structures under arid and semi-arid conditions. The influence of temperature on dynamic behavior of the double-arced concrete dams for instance, is shown to have a direct impact on the thermo-elasticity characteristics of the mixed concrete and the constituting materials in the concrete. Because of these factors the present paper investigates the effects of variation in air temperature on dynamic behavior of these structures based on the data obtained from the precision tools incorporated in the system. Results showed a higher temperature at the upper water height than the deeper water bodies and as such, the size of the cracks greater than the lower depth. For this reason, data obtained from the dilatometer tools shown more drainage of water from the cracks at the upper than the lower level. Results further indicated a variation in the dam configuration at upstream due to higher temperature during summer on one hand and further variation at downstream during winter season. Research shown the vital importance of the precision tools in identifying the effects of components such as variations in temperature and seepage rate in the sustainability of these structures on one hand and their operational efficiency aimed at optimum utilization of the renewable water resources on the other.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.353

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 integrity0.0000.001
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 head0.238
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 designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

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