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Record W2288117343 · doi:10.3390/fib4010006

Properties of Fiber-Reinforced Mortars Incorporating Nano-Silica

2016· article· en· W2288117343 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFibers · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Manitoba
FundersQueen's UniversityQueen's University Belfast
KeywordsMortarNano-Materials scienceFiberComposite material

Abstract

fetched live from OpenAlex

Repair and rehabilitation of deteriorating concrete elements are of significant concern in many infrastructural facilities and remain a challenging task. Concerted research efforts are needed to develop repair materials that are sustainable, durable, and cost-effective. Research data show that fiber-reinforced mortars/concretes have superior performance in terms of volume stability and toughness. In addition, it has been recently reported that nano-silica particles can generally improve the mechanical and durability properties of cement-based systems. Thus, there has been a growing interest in the use of nano-modified fiber-reinforced cementitious composites/mortars (NFRM) in repair and rehabilitation applications of concrete structures. The current study investigates various mechanical and durability properties of nano-modified mortar containing different types of fibers (steel, basalt, and hybrid (basalt and polypropylene)), in terms of compressive and flexural strengths, toughness, drying shrinkage, penetrability, and resistance to salt-frost scaling. The results highlight the overall effectiveness of the NFRM owing to the synergistic effects of nano-silica and fibers.

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 score0.011
Threshold uncertainty score0.513

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 head0.203
Teacher spread0.186 · 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