Comparative performance of normal and ultra-high-performance fibre reinforced concrete slabs to ballistic impact of steel-inset ammunition
Bibliographic record
Abstract
Ultra-High-Performance Concrete (UHPC) is a widely researched material with varied Civil and Military applications for resilient infrastructure. Design and assessment of UHPC members against ballistic impact of in-service ammunitions is critical to their application to protective structures. Experimental tests are necessary for performance verification and the numerical tools are required to assist with the design. This paper presents experimental and numerical studies on small-scale UHPC panels reinforced with steel fibres (UHPFRC) subjected to steel-inset projectiles. Benchmark concrete slabs with the same geometric parameters made from normal-strength concrete (NSC) were tested to compare the performance of UHPFRC against NSC. The perforation limit thickness of UHPFRC and NSC slabs against steel-inset bullets were determined. The enhanced impact resistance of UHPFRC compared to NSC in terms of depth of penetration (DOP) as well as crater area is most effectively utilized against repeated impacts which is crucial for protective structures. Finite element (FE) models of the slabs were developed and validated against experimental results. An empirical equation to predict DOP was developed with a dimensional analysis of the data collected through experiments and numerical results. The equation can reliably predict the DOP of a UHPFRC panel subjected to impact from deformable steel-inset ammunition of 7.62 mm calibre.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".