A novel two-phase mixture optimization framework for radiation shielding UHPC: Advancing ultra-high-performance shielding materials for improved nuclear safety and security
Bibliographic record
Abstract
By leveraging the exceptional mechanical strength and durability merits of ultra-high-performance concrete (UHPC) to fulfill nuclear safety and security requirements, radiation shielding UHPC (RS-UHPC) promises to advance the safe deployment of nuclear energy. This study presents a novel, two-phase (mortar-to-composite) mixture design optimization framework for RS-UHPC by coupling dry- and wet-particle-packing with Optimal Custom Mixture Design (OCMD). The framework was then implemented to develop new RS-UHPCs incorporating various combinations of magnetite, ferroboron, and ilmenite. To this end, thirty RS-UHPC mixtures were developed and evaluated for workability, density, and compressive strength ( f’ c ) and radiation shielding measured in terms of linear attenuation coefficient ( µ ), thermal neutron absorption cross-section ( ∑ abs ), and fast neutron removal cross-section ( ∑ R ). RS-UHPCs with up to 170 MPa f’ c —with improvement in f’ c by up to 33 %, ∑ R by over 120 %, µ by 57 %, and ∑ abs by nearly 700 %—were achieved. These findings demonstrate the efficiency of the proposed RS-UHPC mixture design framework to deliver a promising ultra-high-performance shielding material for revolutionizing the safety and security of nuclear infrastructure. • A new radiation-shielding UHPC (RS-UHPC) was designed via dry/wet-packing optimization. • The synergistic effects of magnetite, ferroboron, and ilmenite on RS-UHPC were quantified. • RS-UHPCs with up to 170 MPa f ’ c —with improvement in f ’ c of up to 33 % were attained. • RS-UHPCs with up to 120 % higher linear attenuation coefficient ( µ ) were achieved. • Ferroboron-rich RS-UHPCs improved the thermal neutron absorption by over 700 %.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".