Alkali–Silica Reactivity Potential of Reactive and Non-Reactive Aggregates under Various Exposure Conditions for Sustainable Construction
Bibliographic record
Abstract
The alkali–silica reaction (ASR) is a primary cause for premature concrete degradation. An accelerated mortar bar test is often used to access the detrimental phenomena in concrete caused by the ASR of aggregates. However, this test requires a certain environmental conditioning as per ASTM C1260. The objective of this study is to explore the effects of the cement alkali content, exposure solution concentration, temperature, and test duration on mortar bar expansion. Factorial experimental design and analysis was conducted to delineate the effects of the individual factors as well as their interaction. Five different aggregates with various mineralogical properties were used, representing reactive and non-reactive aggregates. Various dosages of cement alkalis (0.40, 0.80, and 1.20 Na2Oe), sodium hydroxide (NaOH) solution concentrations (0.5, 1.0, and 1.5 N), and temperature (40 °C, 80 °C, and 100 °C) were the studied variables. Mortar bar expansion was measured at 3, 7, 14, 21, 28, 56, and 90 days. Mortar bars incorporating Jhelum aggregates incurred expansion of 0.32% at 28 days, proving to be reactive aggregates as per ASTM C1260. Similarly, specimens incorporating Taxila aggregates showed expansion of 0.10% at 28 days, indicating non-reactive nature. It was observed that specimens with Sargodha aggregates showed expansion of 0.27% at 28 days for 0.50 N NaOH solution concentration compared to 0.31% expansion for identical specimens exposed to 1.5 N solution. Moreover, expansion increased with exposure duration for all the tested specimens. Experimental results showed that the cement alkali contents had relatively lesser effect on expansion for 1.0 N NaOH; while, in the case of 0.5 N and 1.5 N NaOH, the cement alkali had a significant effect. It was noted that expansion increased with an increase in the temperature. Jhelum aggregates showed 28-day expansion of 0.290% when exposed to 40 °C, but at a temperature of 100 °C, expansion increased to 0.339%. Factorial analysis revealed that the exposure solution had a major contribution towards the expansion of mortar bar specimens. This study highlights the contribution of various exposure conditions on the ASR expansion, which leads to a decisive role in selecting the aggregate sources for various applications and exposure conditions leading to sustainable construction.
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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 | 0.000 |
| 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) | 0.001 | 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".