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Record W4225128910 · doi:10.11159/icsect22.156

CEB as Potential Alternative Sustainable Material in Construction Industry: Survey on the Level of Acceptance in Kuwait

2022· article· en· W4225128910 on OpenAlexvenueno aff
Rana Ezzdine Lakys, Saad Ahmad, Taha Ahmed

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsConstruction industryConstruction engineeringBusinessEngineeringManufacturing engineeringArchitectural engineering

Abstract

fetched live from OpenAlex

Recently, sustainable affordable housing solutions including sustainable construction materials has been at the core of international debates.Constructors and industries seem to have an increasing interest in the compressed earth block as it has many advantages for constructing a durable, comfortable, and low-cost building, it is a promising way of ameliorating the sustainability performance of buildings.The potential barriers to the usage of this technique need to be identified to facilitate and to promote its adoption in local construction.A questionnaire-based survey was conducted in the state of Kuwait to investigate the level of acceptance to this new product and to identify the major potential barriers as perceived by potential private clients.The preliminary results of the survey were very encouraging showing a high level of acceptance and willingness of the respondents toward the CEB's as a construction material and technique.A detailed analysis of the different impact factors shows that the cost reduction associated with the use of CEB's is the most influencing factor driving the respondents to these results.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

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