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Record W3212424951 · doi:10.18280/ijsse.110506

Numerical and Experimental Predication of the Structural Cracking Within Reinforced Concrete Structure due to Conventional State of Loading

2021· article· en· W3212424951 on OpenAlexvenueno aff
Hussam Ali Mohammed, Hussein Abad Gazi Jaaz, Ali Fadhil Naser, Ayad Ali Mohammed

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCrackingStructural engineeringFinite element methodService lifeDeformation (meteorology)Ultimate loadEngineeringComputer scienceMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

It has been well understanding that the occurrence of various crack patterns in the building during its construction life (from first time of construction up to finishing) then subjected to super imposed load or during the service life. Cracks developed due to exceeding of stresses more than the allowable strength, wherever happened on building component. This research works used the finite element method as a powerful tool to simulate the behavior of full constructed building with both concrete system and brick bearing wall. Where the adopted numerical procedure allows to the users to predict the response of building elements due to conventional state of loading. one of the most important response features was the cracking phenomenon, where the numerical model shown that its capability to predict the cracking sequence from the first time of initiating. The prediction of full response and behavior of each element and their connection, shown that the precise of factor of safety used by the designer, where the analysis prove that the design load was about 67% from the cracking load, and the ultimate load was about 260% from the design load. That will allow more sustainability and stability for long time deformation. Beside the numerical solution, there was an experimental part of study, where site investigation, it shown that all data recorded was constant values and the building was stable. Actually, with no increasing of loading, the building reach its stable state, and defect will not develop. That basically because of good design within conventional state of loading.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.291

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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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