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Condition assessment of two prestressed concrete slabs after 60 years in service

2022· article· en· W4312530097 on OpenAlexaffabout
Dana Tawil, Leah Kristufek, Beatriz Martín‐Pérez, Leandro Sanchez, Martin Noël

Bibliographic record

VenueMATEC Web of Conferences · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNondestructive testingPrestressed concreteSlabCorrosionBridge deckUltrasonic testingStructural engineeringVisual inspectionService lifeForensic engineeringBridge (graph theory)EngineeringEnvironmental scienceCivil engineeringMaterials scienceDeckUltrasonic sensorComputer scienceReliability engineeringComposite material

Abstract

fetched live from OpenAlex

Two concrete deck slabs extracted from a Canadian bridge have been evaluated using non-destructive testing (NDT), followed by destructive testing. Throughout its service life, the bridge experienced harsh environmental conditions with frequent freeze-thaw cycles, hot and humid summers, and the use of de-icing salts during winters. This study presents the preliminary results of an exhaustive condition assessment of two prestressed concrete slab panels, where NDT techniques (visual assessments, electrochemical and concrete soundness tests) have been conducted prior to destructive testing. Although visual inspection did not indicate poor concrete quality, the results of the ultrasonic pulse velocity testing showed that both concrete slabs are of poor quality and may be suffering from internal defects. Chlorides may have also been introduced through the strand conduit anchor points. These findings raise concerns regarding the structural integrity of corrosion-affected members.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes2
Has abstractyes

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