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Simultaneous estimation of hygrothermal properties of a prefabricated lightweight wall using one-year on-site measurements to solve inverse problems

2024· article· en· W4399284370 on OpenAlexafffund
Nícolas Pinheiro Ramos, Leonardo Delgadillo Buenrostro, Sandro Metrevelle Marcondes de Lima e Silva, Louis Gosselin

Bibliographic record

VenueBuilding and Environment · 2024
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsUniversité Laval
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsInverseInverse methodEstimationStructural engineeringInverse problemMaterials scienceEnvironmental scienceCivil engineeringComputer scienceEngineeringMathematicsApplied mathematicsGeometryMathematical analysisSystems engineering

Abstract

fetched live from OpenAlex

This study reports on using inverse analysis to experimentally estimate the in situ hygrothermal properties of a prefabricated lightweight building envelope. On-site temperature and relative humidity data were measured for one year in the walls of an occupied detached house. Field measurements were then used to solve inverse problems for identifying effective specific heat, thermal conductivity, vapor resistance factor, as well as heat and moisture transfer coefficients. Fully coupled heat and moisture phenomena were analyzed. The importance of estimating hygrothermal properties for reliable model calibration in hygrothermal performance assessment was addressed. For this purpose, the annual heat and moisture fluxes through the interior wall surface were calculated using the effective properties estimated in this study and reference values from the literature. The greatest deviations were found in cold periods for the thermal response and warm periods for the hygric response. Literature benchmarks were shown to potentially overestimate the annual heat loss by 30 % and the annual moisture transport by 24 %. The present study shows how in situ hygrothermal characterization enables more accurate predictions of the hygrothermal behavior of wall assemblies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.689

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 head0.036
GPT teacher head0.211
Teacher spread0.175 · 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 designBench or experimental
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

Citations6
Published2024
Admission routes2
Has abstractyes

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