Ultra-high-performance concrete for nuclear applications: A review of raw materials and mix design approaches
Bibliographic record
Abstract
The nuclear sector, as a pivotal provider of clean energy, plays a crucial role in advancing green economies. This sector is currently experiencing a resurgence, particularly with the recent emphasis on small modular reactors (SMRs), signaling a significant momentum toward nuclear energy expansion. However, ensuring the safety of nuclear facilities remains critical for the responsible deployment of this technology. Radiation Shielding Ultra-High-Performance Concrete ( RS-UHPC )—an advanced type of concrete engineered for optimal packing density to enhance mechanical properties and durability while effectively attenuating radiation—emerges as a promising solution for reinforcing the Defense in Depth ( DiD ) strategy in nuclear infrastructure. Nevertheless, the existing understanding of RS-UHPC is limited and dispersed. By the time of writing this review, the authors are unaware of any comprehensive review on the subject. This study aims to fill this gap by providing a thorough review of RS-UHPC formulations, critically analyzing existing literature, and identifying key RS-UHPC ingredients and mixture design techniques that influence RS-UHPC properties. Research priorities were identified to further advance RS-UHPC formulation, focusing on specific enhancements in mechanical, durability, and radiation shielding performance. The findings summarized herein contribute to a deeper understanding of the RS-UHPC compositional domain and mix design approaches, ultimately facilitating the achievement of desirable RS-UHPC performance and enhancing the DiD of nuclear facilities. • Radiation-shielding UHPC (RS-UHPC) is promising for enhancing nuclear safety. • This review provides insights on raw materials and their influence on RS-UHPC performance. • Some heavyweight aggregates decrease fluidity, requiring HRWRA adjustment. • RS-UHPC compressive strength is influenced by heavyweight aggregates and nanofillers. • Heavyweight aggregates significantly increase gamma radiation shielding efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".