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Record W4366506674 · doi:10.11159/icsect23.146

Investigation of the Separation Distance for Preventing Seismic Effects on Reinforced Concrete Buildings

2023· article· en· W4366506674 on OpenAlexvenueno aff
Tolunay Ibrahimagaoglu, Arcan Yanik

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEarthquake and Disaster Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSeparation (statistics)Reinforced concreteStructural engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

In this paper, the importance of leaving a separation (earthquake joint) distance in the design against structural movements and the damages caused by earthquakes were examined.Possible collision and pounding scenarios between adjacent buildings that can occur through various combinations were presented by the help of figures.The consequences that may arise if necessary precautions are not taken were explained.Two adjacent buildings with different story heights and dynamic characteristics were modelled separately with ETABS software in accordance with Turkish Seismic Code (TBDY-18).In addition, earthquake analysis was performed.The building models consisted of two adjacent blocks.During the design reinforced concrete columns with beam slab system were modelled as structural members.Reinforced concrete columns and beams were implemented to the system as frame elements.It was clearly seen that the results obtained with the empirical formulas specified in the regulations and the results of the detailed analysis are not matching with each other.

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)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.238
Teacher spread0.228 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicEarthquake and Disaster Impact StudiesFrench-language works237,207