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Enhancing the Impact Resistance of Historical Stone Masonry Units with Fibre-Reinforced Hydraulic Lime Mortars

2011· article· en· W1996353118 on OpenAlexafffund
Vivek Bindiganavile, Md Toihidul Islam, Rachel Chan

Bibliographic record

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsUniversity of Alberta
FundersPublic Works and Government Services Canada
KeywordsMortarFlexural strengthMaterials scienceComposite materialMasonryLimePortland cementBond strengthToughnessCementGeotechnical engineeringStructural engineeringGeologyAdhesiveEngineeringMetallurgy

Abstract

fetched live from OpenAlex

This paper describes the dynamic response of sandstone masonry units bound with fibre-reinforced mortars comparing a Portland cement-lime system with hydraulic lime. A drop-weight impact machine was used to generate stress rates up to 107 kPa/s. The dynamic impact factor and stress rate sensitivity were evaluated for the flexural strength of the sandstone and mortar, and for the bond strength of the unit, and further, the pattern of failure was noted in the units for each mortar mix and loading rate. Polypropylene microfibres were incorporated at 0%, 0.25% and 0.5% volume fraction into the mortar. Results show that the flexural bond strength was more sensitive to stress rate than the flexural strength of the mortar, at similar rates of loading. Further, the stress rate sensitivity of the bond strength decreased with an increase in the fibre content. Also, whereas the flexural toughness factors for the stone-mortar bond fell with fibre reinforcement in the stronger Portland cement-lime system, the bond improved with fibre addition when employing hydraulic lime mortar.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.021
GPT teacher head0.190
Teacher spread0.170 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

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