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Record W3110928573 · doi:10.18280/ijsse.100509

A Comparative Analytical Study of Some External Finishing (Cladding) Material in Terms of Their Ability to Spread Fire in Multi-story Building Facades in Iraq

2020· article· en· W3110928573 on OpenAlexvenueno aff
Ahmed A. Alfakhry

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
FundersUniversity of Mosul
KeywordsCladding (metalworking)BrickMasonryEngineeringFire performanceFire safetyCementCivil engineeringArchitectural engineeringForensic engineeringStructural engineeringFire resistanceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The traditional building in Iraq characterised by the using of a certain number of finishing's in external façades like cement plastering, limestone and perforated yellow brick because the raw materials of manufacturing are available locally. Fire spread through the facades is widely recognized as one of the fastest pathways of fire spreading in the buildings, so the appropriate choose of highly performance finishing material against fire will be potentially the main factor in controlling the fire and suppressed it. This study uses computer modeling and fire simulation technology of Pyrosim, FDS and smoke view to compare the difference between the performance of some traditional finishing materials like cement plastering, limestone and perforated yellow brick with the ACPs and their speed to transfer fire from floor to floor if used in multi storey buildings. The study highlighted that traditional finishing materials are more efficient than modern cladding materials and that the performance of ACP-PE is the worst among the materials examined by this study. Moreover, the cement plastering is the worst among the traditional local finishing materials.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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