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Record W4385335380 · doi:10.1002/eqe.3980

Lateral load resistance of reinforced concrete columns with brittle details

2023· article· en· W4385335380 on OpenAlex
Farah Dameh, S. J. Pantazopoulou

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.

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsYork University
Fundersnot available
KeywordsStructural engineeringParametric statisticsDeformation (meteorology)Benchmark (surveying)ReinforcementBrittlenessFinite element methodDuctility (Earth science)Seismic loadingEngineeringGeotechnical engineeringGeologyCreepMathematicsMaterials scienceStatistics

Abstract

fetched live from OpenAlex

Abstract Analytical models for shear strength and deformation capacity estimation of reinforced concrete members are essential for the practical implementation of performance‐based seismic evaluation of existing structures and form a key part of current seismic assessment codes (e.g., EN 1998‐3 (2005), (2022), ASCE/SEI 41‐17). Although these models have been derived from regression or calibration of large datasets collected from experimental literature, discrepancy persists between experimental and analytical estimates, especially in cases of members with old‐type detailing. The evident uncertainty about parametric dependencies of the underlying mechanistic problem motivated the present work, where a set of benchmark columns that represent older structural detailing are studied parametrically through advanced finite element simulation. Parameters considered were, the axial load ratio, transverse reinforcement amount and spacing, longitudinal reinforcement ratio, and anchorage/lap splice detailing; results were gauged in terms of the resulting drift ratio (deformation capacity) at the performance limit states and were compared with the Code estimates to evaluate their limits and conservatism. The simulation results were used to vet the assumptions that underlie the simple mechanistic models used in deriving seismic design expressions for yield and ultimate deformation capacities, shear strength, and the rate of its degradation with increasing ductility.

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 score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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