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Record W2890869392

Ice wear and abrasion of marine concrete: design of experimental apparatus and procedures

2018· dissertation· en· W2890869392 on OpenAlexfundno aff
Amanda Ryan

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaResearch and Development Corporation of Newfoundland and Labrador
KeywordsAbrasion (mechanical)Work (physics)Computer scienceEngineeringStructural engineeringGeotechnical engineeringMarine engineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Abrasion of marine concrete structures from passing ice is an ongoing problem that leads to loss of structural integrity over time. The purpose of this study is to develop new experimental approaches and apparatus that would allow long term testing of ice wear on concrete samples as a prelude to a larger study that will investigate the wear of concrete by ice. This thesis has drawn on the experiences of previous work to identify the important issues, including those areas where different approaches may be beneficial. A review has been completed of previously used tests setups, contributing factors and areas of uncertainty. This has resulted in two conceptual designs that approach the problem from slightly different angles. The first is a lab scale apparatus that aims to standardize the testing methods for concrete wear due to ice. The conceptual design of a new apparatus will allow wide ranging load applications, long test durations and the inclusion of surrounding water. The second is an in-situ apparatus that has been developed to allow direct comparison between concrete mixtures in terms of wear resistance under realistic but uncontrolled ice conditions. Pilot experiments have been completed and reviewed to refine the initial concept and to determine effective means of abrasion measurement. These experiments provided insight into important features of the designed apparatus, trial results from measurements of iceinduced wear on concrete and useful information on concrete wear experiments in general.

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 categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.247
Teacher spread0.230 · 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.

Study designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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