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Impregnation Technique Provides Corrosion Protection to Grouted Post-Tensioning Tendons

2018· article· en· W4242714925 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

VenueMATEC Web of Conferences · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsGroutCorrosionService lifeEngineeringGeotechnical engineeringStructural engineeringBridge (graph theory)Tension (geology)Forensic engineeringMaterials scienceComposite materialCompression (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

Thousands of bridge structures rely on grouted post-tensioning (PT) tendons. Problems with grouting techniques and grout materials have resulted in bridges with grout voids, chloride contaminated grout and soft grout. These problems have promoted corrosion and failure of post-tension tendons, some within 6 to 17 years of service. The Florida Department of Transportation (FDOT) has spent more than $55 million (USD) repairing 11 post-tensioned bridges to date. A cost-effective corrosion mitigation technique has been developed to minimize the corrosion of PT tendons and extend the service life of bridges which have grouting issues. This paper describes the development and implementation of this technique on grouted PT bridges.

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 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 score0.066
Threshold uncertainty score0.478

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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