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Record W4411694112 · doi:10.1680/jmacr.25.00077

Impacts of anisotropy and exposure conditions on ASR-induced deterioration in columns from the Robert-Bourassa/Charest overpass after nearly 50 years of service

2025· article· en· W4411694112 on OpenAlexaffabout
Rennan Medeiros, Ana Bergmann, O. D. Olajide, Cassandra Trottier, Leandro Sanchez, Benoît Fournier

Bibliographic record

VenueMagazine of Concrete Research · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsInstitut National de la Recherche ScientifiqueConcordia UniversityUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsAnisotropyForensic engineeringService (business)EngineeringStructural engineeringEnvironmental sciencePhysicsBusinessOptics

Abstract

fetched live from OpenAlex

The anisotropic behaviour of 50-year-old reinforced concrete bridge columns affected by alkali–silica reaction (ASR) was investigated, focusing on the Y-shaped columns of the Robert-Bourassa/Charest overpass in Québec, Canada under different exposure conditions. A multi-level assessment approach, incorporating microscopic and mechanical evaluations, was employed to evaluate ASR-induced damage, with an emphasis on anisotropic expansion, stress redistribution, mechanical property degradation and cracking orientations. Diagnostic tools such as the stiffness damage test and damage rating index provided insights into internal microcracking and reduction of the modulus of elasticity, accounting for the influence of environmental exposure conditions. Measurements of strain and residual tensile strength from reinforcement stirrups further revealed the structural implications of ASR anisotropic progression, including reinforcement embrittlement and loss of confinement. The findings highlight the critical role of exposure conditions, reinforcement configuration, and geometric constraints in shaping the deterioration patterns of ASR-affected concrete.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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