Impact Behaviour of Steel-Fibre-Reinforced Alkali-Activated Slag Concrete Exposed to Elevated Temperatures
Bibliographic record
Abstract
Concrete protective structures are mainly meant to withstand impact loads. However, fire events weaken concrete and reduce its impact resistance. This study investigated the impact behaviour of steel-fibre-reinforced alkali-activated slag (AAS) concrete before and after exposure to elevated temperatures (i.e., 200 °C, 400 °C, and 600 °C). Hydration products' stability under elevated temperatures, their effects on the fibre-matrix bond, and, consequently, AAS's static and dynamic responses were investigated. The results reveal that adopting the performance-based design concept to achieve a balance between AAS mixtures' performance under ambient and elevated temperatures is a crucial designing aspect. Advancing hydration products' formation will increase the fibre-matrix bond at ambient temperature while negatively affecting it at elevated temperatures. High amounts of formed and, eventually, decomposed hydration products at elevated temperatures reduced the residual strength due to lowering the fibre-matrix bond and developing internal micro-cracks. Steel fibre's role in reinforcing the hydrostatic core formed during impact loads and delaying crack initiation was emphasized. These findings highlight the need to integrate material and structure design to achieve optimum performance and that low-grade materials can be desired based on the targeted performance. A set of empirical equations for the correlation between steel fibre content in the AAS mixture and corresponding impact performance before and after fire exposure was provided and verified.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".