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Record W1967768487 · doi:10.1139/l08-019

Seismic performance of a full-scale, reinforced high-performance concrete building. Part II: Analytical study

2008· article· en· W1967768487 on OpenAlexafffundvenue
Sébastien Mousseau, Patrick Paultre, Jacky Mazars

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsSNC-Lavalin (Canada)Université de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringFinite element methodContext (archaeology)Seismic analysisDisplacement (psychology)Full scaleComputer scienceScale (ratio)Earthquake engineeringSeismic loadingDuctility (Earth science)EngineeringGeology

Abstract

fetched live from OpenAlex

Full-scale tests provide valuable information on the characteristics of building structures that can be used to evaluate design methods, to calibrate modelling techniques, and to determine damage corresponding to loading levels. These tests are scarce due to the enormous requirements in testing space and specialized testing equipment. The seismic behaviour of a full-scale, two-storey, reinforced high-performance concrete building designed with moderate ductility detailing is evaluated by pseudo-dynamic testing, during which increasing seismic loads are applied, resulting in increasing levels of permanent damage to the structure. This paper presents the analytical predictions of the test results using a global force–displacement parameters approach and a refined approach, half-way between global modelling and finite element modelling, using force–strain parameters and damage mechanics principles. Identification of the parameters required to describe the response parameters are presented together with a description of the numerical procedures used in each approach. It is shown that the predictions are in good agreement with the test results. Advantages and disadvantages of each approach are highlighted in the context of performance-based analysis and design.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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 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

Citations9
Published2008
Admission routes3
Has abstractyes

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