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Record W7042435072

Physical salt attack on concrete: field case study and innovative mitigations

2022· dissertation· en· W7042435072 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsCementitiousSalt (chemistry)Range (aeronautics)SulfateStructural integrityScaling
DOInot available

Abstract

fetched live from OpenAlex

Physical salt attack (PSA) is a physical damage that affects elements associated with extensive sources of deleterious salts and exposed to cyclic environmental conditions. Although this topic has caught the attention of several researchers, some areas still need to be addressed (e.g., using surface treatments for protecting concretes prepared with supplementary cementitious materials [SCMs]). In this thesis, previously repaired friction piles were selected in Winnipeg, Manitoba, Canada, to investigate the associated damage consequences and recommend effective repair techniques. In addition, an experimental program was designed to examine the efficiency of some innovative treatments (nanocomposites) for mitigating/preventing deterioration resulting from PSA; also, the developed treatments were tested in salt-frost scaling conditions to examine their functionality for a wider range of applications. It was found that the field case suffered from destructive PSA consequences, which might compromise the structural ability of some elements (i.e., piles), although it was repaired previously. Also, signs of chemical sulfate attack were detected in the cores of piles as an accompanied damage mechanism (minor effect). Maintenance procedures were recommended to protect the damaged piles from any further deterioration, including removal of deteriorated concrete from the surface, confining the affected piles with high-performance concrete (HPC) with a low water-to-binder ratio (0.30 to 0.40) and appropriate thickness up to the grade beams level and applying an effective surface treatment (e.g., epoxy, ethyl silicate, or experimentally investigated nanocomposites. The results of the experimental study showed that using a high water-to-binder ratio produced vulnerable concretes against PSA, especially with high replacement ratios (e.g., 40% and 60%) of SCMs. The application of ethyl silicate or high-molecular-weight methyl methacrylate did not provide adequate protection against either PSA and salt-frost scaling exposures; however, incorporating nanoparticles (i.e., halloysite-based nano-clay or montmorillonite-based nano-clay) resulted in moderate to superior performance compared to neat coatings. Ethyl silicate-based nanocomposites were efficient in mitigating or fully protecting specimens exposed to both exposures, especially the one prepared with halloysite-based nano-clay at the lowest dosage (i.e., 2.5%). High-molecular-weight methyl methacrylate-based nanocomposites only succeeded in mitigating the consequences of PSA; however, they failed to show the same performance in the salt-frost scaling exposure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.025
GPT teacher head0.272
Teacher spread0.248 · 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 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
Published2022
Admission routes1
Has abstractyes

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