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Record W2938492787 · doi:10.3390/buildings9040087

Passive Ambient Comfort and Correlation of Strategies and Vernacular Devices for Habitat Design in Arid Zones: The Case of Biskra, Algeria

2019· article· en· W2938492787 on OpenAlexaff
Belkacem Berghout, Daniel Forgues

Bibliographic record

VenueBuildings · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsArchitectural engineeringVernacularProcess (computing)Energy consumptionEnergy (signal processing)AridComputer scienceEnvironmental resource managementEngineeringEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Current concerns focus on the need to reduce energy consumption in construction and over the lifespan of buildings. A major objective is to create affordable housing. However, reducing the energy needs of the present without harming the needs of future generations remains difficult to put into practice, especially at the level of habitats in arid zones. In this research, a housing design assistance process for building designers is proposed, with the aim of converging towards a global optimum for the correlation and integration of vernacular devices and strategies in an optimized way. This will allow the integration of energy performance indicators and user comfort, in order to ultimately optimize, for this case study, the morphogenesis of residential buildings based on the morphological structure of a proposed building in Biskra, Algeria. This last developed process is articulated on various combinations distinguished for the case of the integration of devices and strategies, which allow maximum energy saving while ensuring the comfort of the occupants. As part of this research, this integration constituted a major challenge and made it possible to achieve a reduction in energy ratio of up to 25.11% compared to the standard building and the objectives of the research.

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 score0.110
Threshold uncertainty score0.264

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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