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Record W2615346041 · doi:10.1002/eqe.2912

Damage mechanics applied to performance‐based design of reinforced concrete columns

2017· article· en· W2615346041 on OpenAlex
Luis Ignacio Cardona, Patrick Paultre, Jacky Mazars

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDamage mechanicsStructural engineeringDuctility (Earth science)Finite element methodDisplacement (psychology)Computer scienceReinforced concreteFlexural strengthStrength of materialsMaterials scienceEngineeringCreepComposite material

Abstract

fetched live from OpenAlex

Summary Performance‐based design methodology is based on reaching performance objectives that are associated to certain damage conditions. These performance objectives are related to the seismic hazard and to the performance levels. In actual application, reliable tools are required for capturing the evolution of the damage condition as well as for measuring and locating it. Moreover, it is essential to accurately establish the relationship between the damage and the performance levels. This paper shows the application of damage mechanics to performance‐based design. A layered damage mechanics‐based finite element program is presented with a discussion on modeling for prediction of the response of normal‐strength and high‐strength concrete columns subjected to cyclic flexural loading and various axial load levels. The damage indices derived from these analyses were used to elaborate several damage charts expressed as a function of drift and displacement ductility. This makes it possible to establish a relationship between the damage state and the performance levels. Results have demonstrated the ability of the damage mechanics modeling to accurately predict the behavior of the specimens tested. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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 score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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