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Record W4311844233 · doi:10.1139/cjce-2022-0133

A concrete sandwich wallboard damage identification method based on strain energy density increment

2022· article· en· W4311844233 on OpenAlexvenueno aff
Daiyu Zhou, Qun Xie, Xin Wang

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsCrackingStructural engineeringStrain energy density functionMaterials scienceCompression (physics)Strain energyStrain (injury)ModulusSensitivity (control systems)Geotechnical engineeringComposite materialForensic engineeringEngineeringFinite element method

Abstract

fetched live from OpenAlex

During the loading process of concrete members, the identification of characteristic damage states is not sensitive enough. This paper investigates the damage evolution of four concrete sandwich wallboards subjected to vertical compression by employing the strain energy density increment to enhance identification sensitivity. First, the equivalent elastic modulus model for a wallboard subjected to vertical compression is established based on the material stress–strain relationship. Then, the strain data of concrete, obtained from experiments and numerical simulations, are modeled as the normalized unit approximate strain energy density increments (NUASEDIs), which follow the damage evolution of the wallboard as a more sensitive index. The first cracking formation of wallboard corresponds to the first high NUASEDI. The failure damage state corresponds to the final high NUASEDI, which reflects the process changing from the local to the global failure. Finally, the strain energy density increment theory improves averagely the cracking identification sensitivity of concrete members by 13.32% and the failure identification sensitivity by 3.01%.

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 score0.821
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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