A novel two-phase mixture optimization framework for radiation shielding UHPC: Advancing ultra-high-performance shielding materials for improved nuclear safety and security
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it