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Moisture Buffering Effect of Gypsum Board in a Marine Climate: A Field Experimental Study

2014· article· en· W2083134336 on OpenAlexaffabout
Shahrzad Pedram, Fitsum Tariku

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsEnvironmental scienceHumidityMoistureBuilding envelopeVentilation (architecture)Indoor air qualityDurabilityEnvironmental engineeringArchitectural engineeringEngineeringMeteorologyCivil engineeringComputer scienceThermalMechanical engineering

Abstract

fetched live from OpenAlex

Durability, acceptable indoor air quality, energy efficiency and aesthetics are all pillars of good design in healthy buildings. A new approach for optimizing all four of these pillars is whole-building performance design. This approach involves the consideration of heat, air and moisture (HAM) transfer and control of a building, specifically, how the coupled relations between different transient systems (mechanical system, building envelope, indoor environment, outdoor environment, and occupants) affect the building performance and operation. Ventilation is one of the means of controlling indoor humidity in buildings. Its effectiveness depends on the supply air moisture level and the ventilation rate. The drier the supply air is, the higher its capacity to remove indoor humidity. In a marine climate where the outdoor air is relatively moist, higher ventilation rate is required to achieve the same level of indoor humidity in a cold and dry climate. In this study, the potential benefit of interior gypsum finishing in lowering indoor humidity peaks, through the moisture buffering process, and thereby reducing ventilation rates are investigated. A field experimental study is conducted using two identical test facilities at the Whole Building Performance Research Laboratory in Burnaby, British Columbia to test this hypothesis in a marine climate. Initial benchmarking of the recently commissioned test buildings was undertaken to ensure they behaved similarly under identical conditions. Each building was outfitted with an occupant simulator unit, which provided the humidification that would be produced by occupants. The occupants simulators were programmed based on moisture production data analysis from a real high-occupancy apartment suite, to provide two different moisture generation profiles representing typical and high intensities, scaled down to the size of the test buildings. Following benchmarking, three tests were conducted to evaluate the effect of ventilation rate, moisture generation intensity, and moisture buffering ability of finishing surfaces on indoor moisture levels. Preliminary experimental test results are presented. Future tests will be undertaken to consider other factors such as indoor air quality based on carbon dioxide concentration, heating and ventilation energy consumption, and alternative finishing materials.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.316
Teacher spread0.306 · 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 designObservational
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

Citations4
Published2014
Admission routes2
Has abstractyes

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