Mechanical and durability properties of reactive powder concrete exposed to acid, sulfate, and fire
Bibliographic record
Abstract
Reactive powder concrete (RPC) is an ultrahigh-performance concrete with exceptional mechanical strength and very low permeability, making it a promising material for infrastructure elements exposed to harsh environments. However, its high cement content may increase vulnerability to chemical attacks such as acid and sulfate exposure, while its dense microstructure can lead to internal vapor pressure problems under elevated temperatures. To address these challenges within a single RPC system, this study investigated two complementary modification strategies: using blast furnace slag (BFS) as a partial cement replacement to improve chemical durability against acid and sulfate attacks, and incorporating polypropylene (PP) fibers to enhance resistance to spalling under high-temperature conditions. Three RPC mixtures with varying BFS contents (0%, 10%, and 20%) and three mixtures with different PP fiber dosages (0%, 0.25%, and 0.5%) were prepared. Specimens were exposed to sulfuric acid, sodium sulfate, and direct flame to simulate aggressive environmental conditions that can simultaneously occur in infrastructure applications. Unit weight, compressive strength, and microstructural analyses were conducted to assess deterioration mechanisms and the effectiveness of the modifications. The results showed that moderate BFS replacement can help reduce cement content but may increase susceptibility to chemical attack if used excessively, while PP fibers effectively mitigate internal vapor pressure and reduce spalling at high temperatures. This study demonstrates an integrated approach for optimizing RPC performance under combined chemical and thermal hazards, supporting its safe and sustainable use in critical infrastructure.
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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 | 0.001 | 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".