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Record W3041561299 · doi:10.3390/en13143545

Evaluating Patterns of Building Envelope Air Leakage with Infrared Thermography

2020· article· en· W3041561299 on OpenAlexafffundabout
Milad Mahmoodzadeh, Voytek Gretka, Stephen Wong, Thomas Froese, Phalguni Mukhopadhyaya

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsBuilding envelopeLeakage (economics)ThermographyRetrofittingEnvironmental scienceClassification of discontinuitiesInfraredThermalRoofStructural engineeringEngineeringAcousticsMeteorologyOpticsMathematics

Abstract

fetched live from OpenAlex

The next-generation performance-based building energy codes are focusing on minimizing building envelope air leakage. The quantification of air leakage in buildings is typically performed with a blower door test. However, this test does not provide information about the locations of air leakage. The aim of this study is to demonstrate a method involving qualitative and quantitative components that can be used to characterize locations of air leakage with infrared thermography. Since air leakage can have a significant impact on building energy consumption in cold climates, like in Canada, this approach can quickly inform where air barrier discontinuities occurred during construction or where to selectively target air sealing efforts in existing buildings. The observations from this study are presented, based on a thermographic image analysis during a depressurized blower door test at various pressures, in an attempt to quantify the relative rates of air leakage. The results from the investigation showed that infrared thermography (IRT) was able to discern locations and infer relative ratios of air leakage. The qualitative analysis showed that areas of air leakage are more evident under higher pressure difference. The quantitative approach showed that a minimum of 25 Pa pressure difference was required to detect the air leakage in the vicinity of the window frame, as the surface temperature decreased rapidly (almost 60% of the indoor surface/outdoor air temperature difference) at this pressure. A temperature index was defined to prioritize the areas of air leakage for retrofitting purposes. Furthermore, a thermal image subtraction method was used to determine the characteristics of the cracks based on thermal patterns. Finally, the practical implication of this study, for building developers, home inspectors, property mangers, and homeowners, is the early detection of air leakage for both existing and newly constructed buildings which could result in energy and cost savings.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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

Citations29
Published2020
Admission routes3
Has abstractyes

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