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Record W7036256255

Assessment of building retrofit options using hygrothermal analysis tool

2005· article· en· W7036256255 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
FundersNatural Resources CanadaPublic Works and Government Services Canada
KeywordsBuilding envelopeMoistureDurabilityMasonryEfficient energy useRetrofittingEnvelope (radar)Energy performance
DOInot available

Abstract

fetched live from OpenAlex

As the stock of buildings in our society ages, it is expected that there will be an increase in building envelope rehabilitation work. Such activities represent an ideal opportunity to modify the existing wall system to improve building envelope durability and energy efficiency. This could be done by addition of insulation and sealing air leakage paths. However, there is very little information available on how to assess the moisture and energy (i.e. thermal) performance of retrofitted building envelope assemblies and select the optimum retrofit options that will maximize the long-term moisture performance and the energy efficiency of the retrofitted building envelopes together. This paper presents the findings from a study that has used a two-dimensional hygrothermal simulation tool, hygIRC-2D, to assess moisture and energy performance of retrofitted masonry walls used in high-rise construction for both residential and commercial types of buildings at various Canadian locations. The results from the simulations indicate that, if heat, air and moisture transport properties of the materials and the airflow characteristics of the systems can be defined properly a hygrothermal simulation tool can be used to evaluate the moisture and thermal (i.e. energy) performance of various wall systems and associated retrofit options.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.999

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)0.0020.000

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 head0.020
GPT teacher head0.271
Teacher spread0.252 · 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.

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

Citations7
Published2005
Admission routes4
Has abstractyes

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