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Record W3194919571 · doi:10.3390/su13169342

Earthquake Response Modeling of Corroded Reinforced Concrete Hollow-Section Piers via Simplified Fiber-Based FE Analysis

2021· article· en· W3194919571 on OpenAlexaff
Nicola Scattarreggia, Tianyue Qiao, Daniele Malomo

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsMcGill University
Fundersnot available
KeywordsRebarStructural engineeringCorrosionDuctility (Earth science)Materials sciencePierFinite element methodConcrete coverFibre-reinforced plasticReduction (mathematics)Strength reductionGeotechnical engineeringComposite materialEngineeringCreepMathematics

Abstract

fetched live from OpenAlex

The effect of corrosion-induced damage on the seismic response of reinforced concrete (RC) circular bridge piers has been extensively investigated in the last decade, both experimentally and numerically. Contrarily, only limited research is presently available on hollow-section members, largely employed worldwide and intrinsically more vulnerable to corrosion attacks. In this paper, fiber-based finite element (FB-FEM) models, typically the preferred choice by practitioners given their reduced computational expense, are validated against previous shake-table and quasi-static cyclic tests on hollow-section RC columns, and then used to investigate the influence of corrosion-induced damage. To this end, modeling strategies of varying complexity are used, including artificial reduction of steel rebar diameter, yield strength and ductility, as well as that of concrete compressive strength to simulate cover loss, and ensuing dissimilarities quantified. Pushover and incremental dynamic analyses are conducted to explore impacts on collapse behavior, extending experimental results while accounting for multiple corrosion rates. Produced outcomes indicate a minimal influence of cover loss; substantial reductions of base shear (up to 37%) and ultimate displacement capacity (up to 50%) were observed, instead, when introducing relevant levels of deterioration due to corrosion (i.e., 30% rebar mass loss). Its predicted impact is generally lower when considering more simplified assumptions, which may thus yield non-conservative predictions.

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)0.003
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 score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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