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Record W4293074153 · doi:10.11159/iccste22.182

Experimental Study Of The Effect Of Tensile Strain On The Cracking Of Ultra Highly-Reinforced R/C Components

2022· article· en· W4293074153 on OpenAlexvenueno aff
Theodoros Chrysanidis, Denise‐Penelope N. Kontoni

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCrackingMaterials scienceTensile strainUltimate tensile strengthStrain (injury)Composite material

Abstract

fetched live from OpenAlex

The main objective of this research is to study the phenomenon of cracking in the reinforced concrete (R/C) structural elements, in particular the columns and the walls, and more particularly in the extreme regions of the walls, namely the boundary columns. Various parameters of the phenomenon of cracking will be studied, e.g., load influence, tensile strain, etc. It has to be noted the fact that load application is a monotonic axial tensile loading that simulates the strain condition that takes place at the boundary edges of reinforced concrete seismic walls. Specifically, this type of loading simulates the tensile loading that takes place during the first semi-cycle of loading under seismic dynamic events. Experimental research takes place by the construction and use of a group of 4 experimental specimens subjected to different degrees of elongation. This test group examines the tensile parameter and how it affects the cracking. The test specimens in question are all reinforced with the same ultra-high longitudinal reinforcement ratio (6.03%) and subjected to tensile degrees 10, 20, 30 and 50. Significant conclusions are reached on cracking, e.g., its extent, the size of the cracks, their positions, minimum crack width, maximum crack width, average crack width, number of cracks, etc.

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 score0.103
Threshold uncertainty score0.253

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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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