Blast response of ultra-high performance concrete beams with high-strength steel and varying detailing levels under far-field blast loads
Bibliographic record
Abstract
This paper studies the influence of high-strength steel (HSS) and detailing level on the blast response of ultra-high-performance concrete (UHPC) beams. The variables in the tests included the steel grade (HSS vs. ordinary steel), fiber content (1–3 %), detailing level (blast, intermediate and ordinary) and tension steel ratio (ρ = 1.0–2.4 %). UHPC beams with both ordinary and HSS bars were subjected to shock-tube blast testing, followed by residual static testing to assess post-blast capacity. Key findings indicate that incorporating HSS bars significantly improved blast performance by reducing damage and displacements compared to ordinary steel. Increasing the HSS ratio from 1.0 % to 1.5 % further enhanced blast resistance, prevented bar fracture, and preserved significant residual capacity, leading to more ductile failure. The inclusion of steel fibers enabled relaxed detailing (such as increasing tie spacing to s = d/2 or eliminating stirrups altogether), while still achieving comparable performance to beams with stringent blast detailing. This comparable performance highlights the potential of steel fibers to simplify construction. However, since the beams ultimately failed due to bar fracture, the mechanisms driving bar rupture must be carefully considered when designing the detailing in UHPC beams. Finite-element modelling using LS-DYNA’s Winfrith concrete model for UHPC and the Linear Plasticity for high-strength steel accurately predicted blast responses, including displacements, reaction loads, and damage modes. Overall, the study demonstrates that combining UHPC with optimized fiber content and HSS reinforcement provides a novel solution that enhances blast resilience while permitting for practical and constructible detailing approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".