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Record W2162409500 · doi:10.1139/l03-076

The effect of construction designs on temperature field of a roller compacted concrete dam — a simulation analysis by a finite element method

2003· article· en· W2162409500 on OpenAlexvenueno aff
Y L Chen, Changjiang Wang, Shiwei Li, L J Chen

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsRoller-compacted concreteMass concreteFinite element methodTemperature controlGravity damFortranArch damThermalComputer simulationEngineeringStructural engineeringScheduleGeotechnical engineeringMechanical engineeringComputer scienceMaterials scienceSimulation

Abstract

fetched live from OpenAlex

In this paper a numerical simulation of the construction process of roller compacted concrete (RCC) dams is presented. The following features of construction of mass concrete have been considered: hydration heat, age, placing temperature, starting placement date, and placing speed. A 3-D finite element model of the Long-Tan RCC dam, which is to be built in the Guangxi Autonomous Region in China, was analyzed. Temperature distribution and evolution inside the RCC dam were calculated during and after the completion of the dam. Using Fortran TM code, a 3-D thermal simulation analysis of a high RCC dam can be realized on a computer at the construction site. Based on the real factors during the construction period, engineers can predict the distribution of temperature in the RCC dam. Therefore, engineers can take appropriate measures to control concrete temperature to reduce thermal stress within the dam. The effects of the concrete placing temperature, construction speed, and starting date on the temperature are discussed. It has been found that the maximum temperature in a dam can be reduced by 20% through temperature control measures.Key words: RCC dam, thermal simulation analysis, finite element method, temperature field, construction schedule.

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)0.000
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: none
Teacher disagreement score0.766
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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

Citations12
Published2003
Admission routes1
Has abstractyes

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