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Record W2964968876 · doi:10.1139/cjce-2018-0725

Predicting the seismic collapse capacity of adjacent structures prone to pounding

2019· article· en· W2964968876 on OpenAlexvenueno aff
Farzin Kazemi, Benyamin Mohebi, Mansoor Yakhchalian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringProgressive collapseGeologyVibrationEngineeringReinforced concretePhysics

Abstract

fetched live from OpenAlex

In crowded cities, many structures are often constructed in a very close vicinity; therefore, during severe earthquakes, pounding phenomenon occurs due to out-of-phase vibrations of adjacent structures. In this study, pounding of adjacent structures is investigated up to the occurrence of total collapse. The novelty of this study is performing incremental dynamic analyses to compute the seismic collapse capacities of both pounding structures in one model and removal of the collapsed structure during analysis, and proposing modification factors for modifying median collapse capacity of single structures to consider the effect of pounding, which cannot be considered before the design process. The results indicate that pounding may significantly affect median collapse capacities of structures compared with those obtained by neglecting the pounding phenomenon. Therefore, different modification factors are required according to structural characteristics. It is noteworthy that the results of this study were obtained using simplifying assumptions and should not be generalized.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.449

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 head0.007
GPT teacher head0.173
Teacher spread0.166 · 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 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

Citations33
Published2019
Admission routes1
Has abstractyes

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