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Record W2251345127 · doi:10.1556/1848.2015.6.2.5

Moisture management performance of wood-frame in-fill wall in Shanghai, China

2015· article· en· W2251345127 on OpenAlexaffabout
K. Tiwana, Phalguni Mukhopadhyaya, Ehab Zalok, David van Reenen, Carolyn Copeland, Peter Newman

Bibliographic record

VenueInternational Review of Applied Sciences and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsMoistureCladding (metalworking)Building envelopeEnvironmental scienceWater contentEnvelope (radar)Materials scienceThermalGeotechnical engineeringComposite materialEngineeringMeteorologyGeography

Abstract

fetched live from OpenAlex

This paper discusses the results of a research project which aimed at determining the hygrothermal (i.e. thermal and moisture) performance of the Canadian wood-frame building envelope construction in the city of Shanghai in China. The performance assessments of the wood-frame walls were conducted using the two-dimensional hygrothermal simulation tool called hygIRC -2D. In this study an in-fill type wall was considered and hygrothermal simulations were carried out for the weather conditions of Shanghai. Investigations were conducted to determine the influence of the vapour barrier, exterior stucco cladding material and different types of sheathing boards on the moisture performance of in-fill walls. Additional simulations were carried out to determine the influence of air leakage on the moisture performance of in-fill walls. The outputs from the simulations were analysed with the help of a hygrothermal response indicator called RHT index. It was concluded that the design of the in-fill wall including a rain screen but omitting a vapour barrier is expected to lead to the maximum reduction in hygrothermal loading when exposed to the weather conditions of Shanghai, China.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.285

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueInternational Review of Applied Sciences and EngineeringSame topicHygrothermal properties of building materialsFrench-language works237,207