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Record W7075656001

Developing a Plastic Hinge Model for RC Beams Prone to Progressive Collapse

2015· dissertation· en· W7075656001 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2015
Typedissertation
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
Fundersnot available
KeywordsProgressive collapsePlastic hingeHingeBeam (structure)Bending momentSeismic analysisSpan (engineering)Displacement (psychology)Finite element methodStatically indeterminateBending
DOInot available

Abstract

fetched live from OpenAlex

The US General Service Administration (GSA) 2013 Guidelines specify the procedures and the minimum requirements for the design and evaluation of the new and existing buildings against progressive collapse due to an instantaneous removal of vertical load bearing elements (i.e., columns). The objective of this study is to assess the modeling parameters for reinforced concrete (RC) beams specified in the GSA 2013. Three types of RC buildings located in high, moderate and low seismic zones in Canada are designed according to the 2010 edition of the National Building Code of Canada. They were designed to have ductile, moderately ductile, and conventional seismic force resisting system (SFRS). In total, 27 three-dimensional finite element models are developed using ABAQUS by considering the design variables, such as span length, depth of the section, and the reinforcement ratio. Nonlinear pushdown analyses are conducted by increasing the vertical displacement at the location where the column is removed. The bending moment at the critical section of the beams is monitored throughout the analysis. Based on the analysis results, moment-rotation curve for beam for each type of the building is proposed. In addition, it is found out in the study that the detailing of the seismic design has significant effect on the progressive collapse resistance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.343
Teacher spread0.274 · 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.

Study designNot applicable
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

Citations2
Published2015
Admission routes1
Has abstractyes

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