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Record W2921333877 · doi:10.1680/jcoma.18.00054

Steady-state and dynamic hygrothermal performance of rendered straw bale walls

2019· article· en· W2921333877 on OpenAlexaff
Andrew Thomson, Kris J. Dick, Pete Walker

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStrawMoistureWork (physics)LimeEnvironmental scienceMaterials scienceDynamic insulationWaste managementThermal insulationComposite materialMechanical engineeringEngineeringVacuum insulated panelAgronomy

Abstract

fetched live from OpenAlex

Wheat straw, in the form of compacted bales, is increasingly used as thermal insulation in the external walls of buildings. Common practice is to use a render finish, applied directly to surface of the straw bales, to protect them from decay, and enhance structural performance and fire resistance. Coatings are typically made of water vapour permeable materials, such as lime or earth-based renders. Such coatings should allow water vapour to diffuse through, minimising the risk of liquid moisture build up within the thickness of the wall, reducing likelihood of decay. However, to date there has been very limited scientific study of this behaviour in rendered straw bale walls. The aim of the work presented in this paper was to develop understanding of the hygrothermal performance of lime rendered wheat straw bales. A test panel was subjected to varying environmental conditions, including a thermal shock, dynamic freeze–thaw exposure and hot humid conditions. Key scientific contributions of this work include data on the dynamic and steady-state hygrothermal characteristics wheat straw bale walls, combined with the application of heat and moisture modelling. This work will further support uptake of straw bale construction by designers and their wider use in energy-efficient construction projects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.731

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 communication0.0000.001
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 head0.005
GPT teacher head0.173
Teacher spread0.168 · 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 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Construction MaterialsSame topicHygrothermal properties of building materialsFrench-language works237,207