Blast performance of high-strength concrete beams with ASTM-A955 stainless-steel rebar and improved detailing
Why this work is in the frame
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Bibliographic record
Abstract
This research has studied the effects of improved blast detailing on the blast-behaviour and failure mechanisms of high-strength concrete beams with stainless-steel (SS) rebar meeting ASTM-A955. The test variables included the detailing level (blast and ordinary detailing), steel-fibers, steel type (SS vs. ordinary steel), SS steel ratio (ρ = 1% and 1.5%), SS alloy type (XM-28 and 2304) and type of blast test (repeated vs. single). Blast detailing consisted of introducing compression bars and transverse steel at d/4 spacing to strengthen the midspan compression zone, while fibers were used to relax tie spacing to d/2. Beams with ordinary detailing were singly-reinforced and had stirrups in the shear-regions only. The experiments included replicate beams tested under shock-tube and static conditions. Residual static tests were also conducted on the beams after the blast tests. The findings show that improved detailing in stainless-steel RC beams allowed for large ductility under static loads, while increasing blast-resistance, reducing displacements, and resulting in remarkable post-blast resistance. Introduction of fibers further improved static and blast performance, and was effective in reducing damage and blast-displacements in beams with larger tie spacing, thereby demonstrating their ability to simplify construction. Increasing the steel ratio increased static-load capacity and blast resistance, while high ductility was achieved under static and blast loads, regardless of SS alloy type. The results also show that replacement of ordinary bars with stainless rebar enhanced blast behaviour, by increasing blast capacity and reducing blast-deformations. In the numerical study, the responses of the beams were simulated using FE software VecTor2 with accurate predictions of behaviour under quasi-static and extreme-blast load conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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