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Record W4406525716 · doi:10.70961/gvur1794

Study of the dynamic thermal behavior of the walls of standard constructions in Madagascar

2024· article· en· W4406525716 on OpenAlexaff
RAHELIARILALAO Bienvenue

Bibliographic record

VenueInternational Journal of Engineering Sciences and Technologies · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsCentre de Recherche Industrielle du Québec
Fundersnot available
KeywordsThermalGeologyMaterials scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

Passive housing guarantees a comfortable indoor climate in all seasons without having to resort to expensive systems in terms of energy consumption.Building materials, thanks to their thermal inertia, the ability on the one hand to slow down and attenuate the propagation of thermal wave fluctuations and on the other hand to absorb and store energy, play a major role in this sense.The object of this article is to determine the thermal inertia of earth-based walls, shaped in brick or not, typical envelopes of dwelling houses in Madagascar.The study is based on the dynamic thermal characteristics defined by the international standard ISO 13786.This also specifies the methodological for their calculations.The results are intended to guide the choice integrating thermal comfort with low energy consumption in future constructions.They show that the thickness of the wall has a significant effect on the time lag as well as the decrement factor of the daily variations of the temperature.It follows that, compared to brick walls, rammed earth walls present a greater capacity to absorb, store and release heat.It can be deduced that building designed with such an envelope provide better thermal comfort during hot seasons in the highlands where tropical climate is tempered by altitude and throughout the year in other regions.These encouraging results prove that traditional earth constructions have their place in sustainable building today.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.132

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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