MétaCan
Menu
Back to cohort
Record W4409501179 · doi:10.5006/c2022-17781

Investigation & Mitigation of Corroding Unbonded Post-Tension Tendons

2022· article· en· W4409501179 on OpenAlexaff
Lenard Mancs, David Whitmore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsTension (geology)Materials scienceStructural engineeringComposite materialComputer scienceEngineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

Abstract Unbonded post-tension (PT) tendons have been used for many years to reinforce concrete structures. Generally, these structures have performed well except where unbonded PT tendons have suffered from corrosion due to moisture penetration or protective grease deficiencies. Like other technologies, unbonded PT systems have evolved and improved over the years from “paper-wrap” to “push-through” to “heat sealed” to “extruded” to “fully encapsulated” systems. Consideration should be given to the type of system when selecting the appropriate evaluation and corrosion mitigation methods for these systems. Evaluation of unbonded post-tensioned structures is important to determine the current condition and to determine if corrosion and deterioration is likely to occur. If broken tendons or corrosive conditions are identified, a suitable mitigation strategy should be implemented such that the structural integrity can be maintained, and the service life of the structure can be met or extended. This paper will discuss the types of unbonded PT structures which exist, evaluation techniques which can be used to identify the presence of corrosion, and mitigation methods which have been developed to mitigate corrosion of unbonded PT tendons. Project case studies will be presented to demonstrate the applicability and effectiveness of these techniques.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.201
Teacher spread0.185 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicStructural Load-Bearing AnalysisFrench-language works237,207