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3D Temperature Field Prediction and Experiment Verification of Concrete Diaphragm of Box-Girder Bridge in Early Hardening Period

2013· article· en· W2024975170 on OpenAlexaff
Yi Zhou, Jing Hong Liu

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsBox girderStructural engineeringDiaphragm (acoustics)CrackingFinite element methodHardening (computing)Materials scienceEngineeringGirderComposite material

Abstract

fetched live from OpenAlex

Diaphragm is often used in box-girder bridge for controlling warping stress such those in midspan or transferring strong bearing reactions such those in ends of span. The results of a crack investigation of box-girder bridges showed that vertical cracks can be found on most diaphragms and formed in early stage of the concrete hardening. Temperature caused by hydration heat is an important factor for these cracking. Therefore temperature field prediction for the diaphragm is significant to prevent the concrete diaphragm cracking. In this paper, three-dimensional finite element analysis software ANSYS is used for simulating 3D temperature field of diaphragm of the concrete box girder bridge in all stages of construction. By calculating space temperature field of the diaphragm in different time hydration heat of the law is analyzed, combined with the measured temperature a comparative analysis to verify the validity of the temperature prediction method is conducted. The results show that simulation method is effective and accurate enough to predict the time-varying temperature field of the diaphragm.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.368

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 head0.027
GPT teacher head0.344
Teacher spread0.317 · 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 designBench or experimental
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

Citations0
Published2013
Admission routes1
Has abstractyes

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