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Record W3109124616 · doi:10.1201/9781003078852-55

Envelope air pressure design load: an approach for hygrothermal analysis of retrofitted high-rise masonry wall assemblies

2020· book-chapter· en· W3109124616 on OpenAlexaboutno aff
Réda Djebbar, David van Reenen, M.K. Kumaran, Hengjin Ruan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryEnvelope (radar)Building envelopeStructural engineeringEngineeringMaterials scienceEnvironmental scienceAerospace engineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

This paper presents the analytical approach to predict envelope air-pressure differential that was newly implemented into IRC’s computer weather analysis tool for hygrothermal calculations WeatherSmart-1.1 . In addition to its original features, this tool now allows obtaining in a user-friendly way yearly profiles of hourly envelope air-pressure differential for both low and high-rise buildings. Hourly envelope air-pressure profiles are derived from the knowledge of both indoor and outdoor hygrothermal design loads. WeatherSmart-1.1 is being used within the frame work of a research project to assess effects of adding supplementary insulation and air-sealing retrofit on the long-term hygrothermal performance of different wall types used in Canadian high-rise buildings. Within the frame work of this research project, the paper also introduces how effects of moist air infiltration and exfiltration across tall building envelopes are assessed using IRC’s advanced HAM model hygIRC .

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.207
Teacher spread0.185 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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