Multiaxially Loaded Concrete Undergoing Alkali-Silica Reaction (ASR)
Bibliographic record
Abstract
Alkali-silica reaction (ASR), the predominant type of alkali-aggregate reaction, is a deleterious reaction that causes expansion, cracking, and degradation of mechanical properties of concrete. ASR remains a major problem for concrete structures worldwide. Many factors, such as alkali level, reactive component in aggregate, humidity, temperature, and stress state, affect the manifestation of ASR. While being a serious concern for ASR-affected concrete structures, the effect of stress state on the ASR expansion, cracking, and degradation of mechanical properties of concrete is poorly understood. \nIn this study, the effect of multiaxial stresses on the ASR-induced expansion, cracking, and degradation of mechanical properties of concrete was investigated. A novel method was developed for applying multiaxial (uniaxial, biaxial or triaxial) compressive stresses in concrete specimens and sustaining the stresses for the duration of the reaction. By measuring triaxial expansion, this study elucidated the axial and volumetric expansion of multiaxially loaded ASR-affected concrete. By performing damage rating index (DRI) analysis, ASR cracking was portrayed along three mutually perpendicular planes. ASR expansion and DRI were correlated for multiaxially loaded ASR-affected concrete. Moreover, by testing cores taken along different directions and at different stages of ASR, this study determined the influence of stress on the degradation of mechanical properties. The test results revealed that ASR-affected concrete behaves as an orthotropic material. This study developed an expansion-stress relationship for ASR-affected concrete. The proposed relationship was validated by performing a finite element analysis of the concrete specimens from the experiment. The predicted expansions are in reasonable agreement with the measured expansions. \nThis study also investigated the influence of temperature and of coarse aggregate grading on the ASR performance of concrete. Increasing temperature from 38 to 50 째C shortened the test duration by more than 3 times with little effect on the response of concrete. A 10% deviation in coarse aggregate grading may result in up to 50% larger concrete expansion. Moreover, through microscopic examination, this study explained the mechanism of the partial recovery that followed the loss in the mechanical properties of concrete caused by ASR.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".