Experimental Assessment of the Tensile Bond Strength of Mortar-Mortar Interfaces: Effects of Interface Roughness and Mortar Strength
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".