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Record W2808488941 · doi:10.1520/gtj20170173

Experimental Assessment of the Tensile Bond Strength of Mortar-Mortar Interfaces: Effects of Interface Roughness and Mortar Strength

2018· article· en· W2808488941 on OpenAlexaff
Samuel Bauret, Patrice Rivard

Bibliographic record

VenueGeotechnical Testing Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMortarGeotechnical engineeringUltimate tensile strengthMaterials scienceBond strengthSurface finishComposite materialGeologyAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Concrete gravity dams are subject to external forces that can lead to sliding at the base and overturning about the toe. The latter may result in mobilization of the tensile bond strength of the concrete-rock interface at the dam heel. This article attempts to address this issue by experimentally characterizing the bond strength according to the “concrete” material strength and the interface surface roughness. Experimentation was conducted in a laboratory environment using mortar interface replicas to simulate concrete dams and medium-strength bedrock foundations. Two different mortar strengths and five different roughness profiles were assessed. The surface roughness was characterized using the slope root mean square “Z2” roughness parameter. The bond strength was determined by a direct tensile method. The analysis of variance method was used to assess parameter significance. Results showed meaningful tensile bond strength variation with respect to the interface roughness, but no variation was caused by different material strengths. Reported bond strengths may contribute to increase the accuracy in predicting the tensile bond of a concrete-rock interface. It may help engineers in the field of dam stability make more accurate predictions regarding dam overturning safety factors.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.276
Teacher spread0.259 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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