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Record W2910829630 · doi:10.11159/ffhmt16.153

Effect of Local Temperature during Service on the Mechanical Properties of Concrete

2016· article· en· W2910829630 on OpenAlexvenueno aff
Imane Benoudjafer, Labbaci Boudjema, Ibtissam Benoudjafer, Yacer Labbaci

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Materials scienceComputer scienceBusiness

Abstract

fetched live from OpenAlex

Concrete is one of the most widely used building materials. Extensive researches were accomplished the aggressive effect of very high temperature on the behavior of concrete. However, the effect of local service temperatures remains significant, even very important, if we consider the safety margins provided during the sizing of pieces in the warm regions and their severe climatic conditions of the setting and hardening. In this context, the data on the behavior of the concretes at service temperatures are necessary to predict the safety of the buildings and constructions in various regions. The main objective of this paper is to determine the sensibility of the concrete to its climatic environment, during the period of service in the Saharan regions. It has been shown, by using three different compositions of concrete based on local materials, that the performance of concrete fall considerably with the increase of the temperature until 60 C. A thermal enclosure was conceived; the evolutions of the strength are presented and compared with those obtained for the reference results at 20C temperature. The consequences on the durability and the reliability of structures in these regions are important, which appeals to the necessity in a consideration the risks caused by geo-climatic conditions during the design of structures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.382

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicFire effects on concrete materialsFrench-language works237,207