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Record W3047271179 · doi:10.20381/ruor-25033

The Effect of Alkali-Silica Reaction on Aggregate Interlock In Reinforced Concrete and the Use of Digital Image Correlation to Monitor the Long-Term Behaviour of Concrete Structures

2020· dissertation· en· W3047271179 on OpenAlexfundno aff
Francis Thériault

Bibliographic record

VenueuO Research (University of Ottawa) · 2020
Typedissertation
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
FundersPontifícia Universidade Católica do Rio de JaneiroMitacs
KeywordsAlkali–silica reactionInterlockAlkali–aggregate reactionDigital image correlationAggregate (composite)Term (time)Materials scienceGeotechnical engineeringForensic engineeringStructural engineeringComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Alkali-silica reaction (ASR) is a chemical reaction between the alkali hydroxides from the concrete pore solution and some siliceous mineral phases present in the aggregates used to make concrete. ASR generates a secondary product, the so-called ASR-gel, that swells upon moisture uptake, leading to induced expansion, microcracking, and reduction in the mechanical properties of the affected material. ASR is likely the most harmful damage mechanism affecting the serviceability and long-term performance of concrete infrastructure worldwide, yet its structural implications in concrete structures remain unclear. Even though shear resistance of reinforced concrete has been studied extensively by the research community due to the brittleness and danger associated with concrete shear failures, knowledge on the impact of ASR on reinforced concrete shear resistance is very limited. To fill this lack of knowledge, the effect of ASR on the aggregate interlock shear transfer mechanism in reinforced concrete was investigated. Lightly reinforced shear push-off specimens with low to moderate expansion levels were tested while recording crack kinematics. The experimental testing program allowed to decouple the deleterious effect of ASR microcracks within the reactive coarse aggregate particles and the beneficial effect of the so-called chemical prestressing. The aggregate interlock shear strength was significantly impacted, even in the case of a low expansion level for which the microcracks have theoretically not reached the cement paste yet, and surprisingly, it was not affected by prestressing. The experimental results were compared to predictions from three existing simplified aggregate interlock models which tended to overestimate the measured shear strengths. Digital image correlation (DIC) is an innovative optical measurement technique that could provide several advantages for long-term structural inspections such as remote full-field measurements. A method was proposed to correct 2D-DIC measurement errors associated with the inevitable camera movement between photographs taken during different inspections. Using the aforementioned push-off specimens, it was applied to the monitoring of shear crack kinematics and ASR expansion. The method significantly improved measurements produced from images acquired with a non-expensive hand-positioned camera equipped with a lens of normal focal length and a free to use DIC software. For ASR expansion monitoring, the measurement errors could not be reduced below a selected tolerance limit of ±0.02 mm (±0.01% strain), although increasing the measurement gauge length could potentially provide satisfactory results. On the other hand, over 99 and 96% of the measurements were within the selected tolerance limit of ±0.1 mm for the corrected crack width and slip measurements, respectively. These promising results validate the potential of the proposed method to overcome errors associated with camera movement between photographs and as such, it represents a step towards the use of the DIC technique for periodic structural inspections.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.275
Teacher spread0.254 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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