Effect of Alkali-Silica Reaction on Shear Strength of Reinforced Concrete Structural Members
Bibliographic record
Abstract
Alkali-silica reaction (ASR) was discovered in the early 40s by Stanton (1940) of the California Division of Highways. Since, it has been recognized as a major degradation mechanism for concrete dams and transportation infrastructures. Sometimes described as the ’cancer of concrete’, this internal swelling mechanism causes expansion, cracking and loss of mechanical properties. There are no known economically viable solutions applicable to massive concrete to prevent the reaction once initiated. The e ciency of the mitigation strategies for ASR subjected structures is limited. Several cases of ASR in nuclear generating stations have been disclosed in Japan (Takatura et al. 2005), Canada at Gentilly 2 NPP (Tcherner and Aziz 2009) 1, and more recently, in the United States for which the U.S. Nuclear Regulatory Commission issued Information Notice (IN) 2011-20, ’Concrete Degradation by Alkali Silica Reaction,’ on November 18, 2011, to provide the industry with information related to the ASR identified at Seabrook. Considering that US commercial reactors in operation enter the age when ASR degradation can be visually detected and that numerous non nuclear infrastructures (transportation, energy production) have already experienced ASR in a large majority of the States (e.g., Department of Transportation survey reported by Touma (Touma 2000)), the susceptibility and significance of ASR for nuclear concrete structures must be addressed in the perspective of license renewal and long-term operation beyond 60 years. The aim of this report is to perform an extensive parametric series of 3D nonlinear finite element analyses of three di erent “beam-like” geometries, including two di erent depths, three di erent types of boundary conditions, and four other parameters: namely, the ASR volumetric expansion, the reinforcement ratio, the loss of elastic modulus induced by ASR and the loss of tensile strength caused by ASR.
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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 | 0.000 | 0.001 |
| 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) | 0.004 | 0.001 |
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".