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Record W4399765663 · doi:10.32920/26052478

Thermal Stress Analysis of Reinforced Concrete Members in Cracked Sections

2024· preprint· en· W4399765663 on OpenAlexaff
B. Shahbazian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStress (linguistics)Structural engineeringMaterials scienceGeotechnical engineeringComposite materialGeologyForensic engineeringEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Many reinforced concrete structures are commonly subjected to thermal effects due to various environmental conditions or service functions. Such thermal effects lead to the development of thermal stresses and, consequently, the formation and propagation of cracks in the concrete member. The cracks reduce the member stiffness and thereby alleviate thermal stresses. Most analyses of reinforced concrete structures subjected to thermal gradients employ the conventional linear-elastic uncracked method, which often overestimates the thermal bending moments. A nonlinear analytical approach is proposed to determine thermally induced moments in reinforced concrete structures subjected to simultaneous thermal and mechanical loading. The results obtained from this procedure are compared against those found by the elastic uncracked concrete method for structures such as beams, continuous bridges, and liquid containments. The non-linear cracked concrete method more accurately predicts the flexural response, while the results generated with the linear elastic uncracked method prove to be overly conservative and overestimated.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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