MétaCan
Menu
Back to cohort
Record W3092336021 · doi:10.7492/ijaec.2014.019

Analysis of the Mechanical Behavior of Prefabricated Wattle and Daub Walls

2015· article· en· W3092336021 on OpenAlexvenueno aff
Guadalupe Cuitiño, Graciela Maldonado, Alfredo Esteves

Bibliographic record

VenueInternational Journal of Architecture Engineering and Construction · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsnot available
FundersAgencia Nacional de Promoción Científica y TecnológicaConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsWattle (construction)Materials scienceComposite material

Abstract

fetched live from OpenAlex

This paper summarizes an extensive research about mechanical strength of wattle and daub walls, employed in natural construction.Prefabricated wattle and daub walls, corresponds to the constructive system of frameworks, the perimeter of the walls of enclosures are composed of a wooden frame and the interior is a framework of Castilla canes, to eventually be covered with a mud mixture with clay, sand and vegetable fiber.Building with wattle and daub, is used worldwide, nevertheless, scant information is available about the strength of the walls against compressive and cutting loads.To gather more information about this, prefabricated wattle and daub walls were built in real scale 1:1, and tested in the University structures laboratory.Was obtained that walls have a ultimate compressive strength of 1.56M P a and the average shear strength is 0.13M P a, these values are higher than adobe's values and is recommended this natural construction in areas with earthquakes because its flexibility instead of adobe.These data are valuable for structural calculations of wattle and daub houses and make safer homes.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.197
Teacher spread0.191 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Architecture Engineering and ConstructionSame topicStructural Analysis and OptimizationFrench-language works237,207