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Record W4412754906 · doi:10.11159/iccste25.180

Numerical Investigation on the Influence of Shoring Stiffness on Slab Deflection with Consideration of Construction Phase Loading

2025· article· en· W4412754906 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 · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsShoringStiffnessDeflection (physics)SlabStructural engineeringMaterials scienceEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

The construction phase of reinforced concrete buildings is critical, as temporary loads during shoring and reshoring can induce deflections that impact long-term serviceability.Traditional methods for evaluating these loads rely on assumptions of infinite shoring stiffness and uniform load distribution, which can lead to underestimation of deflections, especially when concrete creep is not considered.This research proposes a finite element modeling (FEM) process, integrating shoring stiffness, time-dependent concrete properties and concrete creep to simulate the load-deflection behavior of multi-story reinforced concrete buildings from the construction phase to the application of the service load.The aim is to study the influence of shoring system stiffness on load distribution and slab deflection.The proposed methodology, implemented in a standard FEM software, is then applied on a typical building for construction phase schemes including one level of shore and one, two and three levels of reshoring.The influence of stiffness variations is studied.It was found that accounting for the stiffness of shoring systems is essential for reliable assessment of slab deflections during the construction phase.

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 score0.188
Threshold uncertainty score0.456

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 head0.015
GPT teacher head0.229
Teacher spread0.215 · 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
Published2025
Admission routes1
Has abstractyes

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