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Record W4327604775 · doi:10.18280/ijsdp.180230

A Practical Alternative Wall System to Promote Buildings Energy Efficiency: A Comparative Environmental Study

2023· article· en· W4327604775 on OpenAlexvenueno aff
Haider I Alyasari, M Altaweel, Ammar K. Dhumad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringEfficient energy useEnvironmental scienceCivil engineeringEngineering

Abstract

fetched live from OpenAlex

The development in science and technology in the building industry has proven that the use of traditional construction methods has become undesirable because of its adverse effects on the building itself in terms of weight and thermal performance, thus affecting the energy efficiency of the building.Reducing energy consumption is a high-priority issue at various societal and economic levels.Thermal performance is the extent to which the design of a building responds to the daily and seasonal changing in climatic conditions.Designing buildings that achieve thermal comfort in harmony with the external environment requires involving appropriate modern technologies.The present simulation-based research assesses thermal performances by testing different wall systems, which are Brick Wall (BW), Cellular Concrete Wall (CCW), Concrete Block Wall (CBW), and Izocrete Block Wall (IBW).The thermal performance of a wall system is characterized by its surface temperature when exposed to solar radiation.Preliminary results indicate that the new technology of wall systems plays a vital role in reducing temperature swings, which leads to reducing the internal temperature and thus promotes the building's energy efficiency.The IBW was found to be efficient and best during the test period, followed by CCW.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.292
Teacher spread0.269 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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