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Record W6928814727 · doi:10.4224/40002686

Impacts of indoor conditions calculation methods on the moisture performance of wood-frame walls

2019· report· en· W6928814727 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2019
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsNational Research Council Canada
FundersInfrastructure Canada
KeywordsASHRAE 90.1Relative humidityMoistureCladding (metalworking)Infiltration (HVAC)MicroclimateHumidity

Abstract

fetched live from OpenAlex

The indoor environmental conditions (temperature and relative humidity) are critical when estimating the hygrothermal performance of building envelopes. Measured data is not always available and as such they are estimated using any of the various model available in the literature. The objective of this work was to compare the influence of indoor conditions calculation methods on the hygrothermal responses and the moisture performance of wood-frame wall assemblies in different Canadian cities (Ottawa, Vancouver and Calgary) under historical climate loads. Various combinations of controlled and uncontrolled indoor temperature and relative humidity were calculated using approaches proposed in the ASHRAE Standard 160. These indoor conditions were implemented as indoor boundary conditions in the simulation of heat and moisture transfer for four different types of wood-frame wall assemblies, assuming no leakage in the vapour barrier. These wall assemblies differ by their cladding types: fiberboard, vinyl, stucco and brick. In each city, simulations were run for two years as selected from a historical climate data set based on the moisture index. The wall orientation receiving the most wind-driven rain for the second year was selected for simulations in each city. A 2-storey residential building having 7 m height above grade, located in suburban area, was considered. The amount of wind-driven rain impinging on the surface of the walls was calculated using the model developed by Straube and Burnett (2000). Material properties were taken from the NRC material property database. Water infiltration through the assembly was assumed to be 1% of the wind-driven rain as suggested by the ASHRAE Standard 160. Temperature and relative humidity of the outer and inner surfaces of OSB sheathing and gypsum board were compared amongst the indoor conditions scenarios. The mould growth risk on the same surfaces was used to compare the influence of different indoor conditions scenarios on the moisture performance of the walls. The indoor temperature profiles calculated for the case where only heating is present fluctuate between 21°C and up to 30°C, depending on the outdoor temperature. With air conditioning, the indoor temperature varies between 21 and 24°C. For the indoor relative humidity profiles, when uncontrolled (no dehumidifier), it can fluctuate between 30 and 70%. With dehumidifier, the indoor relative humidity is either constant or varies within a limited range (45 to 56% in Ottawa, 44 to 52% in Vancouver, and 35 to 41% in Calgary). During the winter and summer periods, the difference between the controlled and uncontrolled relative humidity can be more than 25%. Temperature and relative humidity on the surfaces of the gypsum board reflected that of the indoor conditions. For all the cases analyzed, the indoor conditions did not have any significant impact on the temperature and relative humidity profiles of the OSB, and consequently the mould growth risk did not differ among the different indoor conditions for all cladding analyzed in all cities. When there is no leakage in the vapour barrier, the difference in indoor conditions is reflected mainly on the gypsum panel and hardly reach the OSB panel. Future studies should consider the air leakage as the exfiltration of warm and humid indoor air during the heating season may lead to the condensation in the structure that modifies the hygrothermal response of wall components.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.022
GPT teacher head0.331
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes4
Has abstractyes

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