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Record W2047257222 · doi:10.3992/1943-4618-9.3.100

MECHANICAL PERFORMANCE OF LIME-CEMENT MORTAR FOR STRAW-BALE CONSTRUCTION

2014· article· en· W2047257222 on OpenAlexaff
Colin MacDougall, Stephen Vardy

Bibliographic record

VenueJournal of Green Building · 2014
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsMortarCompressive strengthLimeStrawMaterials scienceMasonryCementCuring (chemistry)Composite materialYoung's modulusGeotechnical engineeringStructural engineeringEngineeringMetallurgyChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Experimental data describing the mechanical performance of Portland cement- hydrated lime mortars used for straw bale construction is presented. Straw bale construction uses stacked straw bales plastered on each side to form load-bearing elements. Mortars used have slumps of approximately 50 mm, compared to slumps up to 279 mm for conventional masonry mortars. Cylinder and cube tests of a range of typical straw bale mortar mixes were carried out. The mortars had compressive strengths ranging between 0.3 MPa and 13 MPa. Empirical equations describing the relationships between compressive strength and curing time, w/cm ratio, proportions of lime, cement and sand, and modulus of elasticity are presented. The data show that cement-lime mortars for straw bale construction will have a higher modulus of elasticity and lower failure strain than a conventional mortar of equivalent compressive strength. The Modulus of Elasticity is on average 818 times the compressive strength of a straw bale mortar, compared to 100 to 200 times as reported in the literature for conventional mortar. The average failure strain for straw bale mortar is 0.00253 compared to 0.0087 to 0.0270 reported in the literature for conventional 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.299

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 head0.008
GPT teacher head0.211
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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