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Record W4285296303 · doi:10.2749/prague.2022.1337

Toward Crack-based Assessment of Shear-distressed Reinforced Concrete Infrastructure

2022· article· en· W4285296303 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReport · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrioritizationReinforced concreteComputer scienceRisk analysis (engineering)Construction engineeringEngineeringStructural engineeringBusinessManagement science

Abstract

fetched live from OpenAlex

<p>The prioritization of repair and rehabilitation efforts for concrete infrastructure is typically informed by damage observed during routine field inspections. Field observations are qualitatively categorized into condition states based on pre-established measurement limits that do little to account for the member-specific details that affect structural behaviour. As a result, conventional strategies do not typically provide reliable, quantitative predictions about the implications of observed damage. Several mechanics-based approaches for the assessment of shear-distressed reinforced concrete structures have been proposed within the last decade. This paper presents an overview and brief comparison of two assessment procedures. Ultimately, this research aims to develop recommendations for refined numerical procedures that assist infrastructure renewal experts to successfully manage the existing infrastructure inventory.</p>

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.

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 score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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