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Record W4400234990 · doi:10.11159/iccste24.234

Finite Element Analysis of Slab Deflection Due To Construction Loads

2024· article· en· W4400234990 on OpenAlexvenueno aff
Jaurelle Keugong Foula, Georges El-Saikaly, Ahmad Abo El Ezz

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicConstruction Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodDeflection (physics)Structural engineeringSlabComputer scienceEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

The construction phase of multi-story reinforced concrete buildings is considered as a critical phase in a building's lifespan.Construction loads are likely to create additional deformations in the slabs, altering their serviceability.Therefore, it is crucial to understand how loads are distributed during construction and to estimate their effect on slab deflection at an early stage in the design process.In this paper, a finite element modeling process is developed to simulate the construction phase of a multi-story reinforced concrete building.The construction scheme involves the use of the shoring and reshoring system to transfer the load of newly cast floors to lower slabs.The model is employed to analyze the effects of construction loads on short-term and long-term slab deflection.The proposed modeling process takes into account construction sequences as well as time dependent properties of concrete.The modeling process is validated by comparing the model results to those of a case study from literature, which involved field measurements of slab deflections during the construction of a 28-story reinforced concrete building in Canada.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.219
Teacher spread0.209 · 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 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

Citations0
Published2024
Admission routes1
Has abstractno

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Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicConstruction Engineering and SafetyFrench-language works237,207