Nonlinear Analysis of Shear-Deficient Beams Strengthened Using UHPFRC under Combined Impact and Blast Loads
Bibliographic record
Abstract
The dynamic response of shear deficient (SD) beams constructed with ultrahigh performance fiber reinforced concrete (UHPFRC) is evaluated in this study. Additionally, a comparison is made with adequately reinforced (AR) beams when subjected to sole impact and blast loads and their combinations using finite-element (FE) simulations in LS-DYNA software. To explore more efficient and optimal strengthening designs using UHPFRC under extreme loads, the influences of the thickness of a UHPFRC cover (tU) and different strengthening schemes of a UHPFRC cover are investigated through a parametric study. Also, a new approach is proposed for calculating the damage index, based on the residual shear capacities of the beams, by performing a multiphase loading procedure to describe the damage states of the UHPFRC-constructed beams quantitatively. From the FE simulations, it is found that the use of UHPFRC in the whole cross-section of the beam has more positive effects on the strength enhancements of the SD beams compared to the AR beam, especially when the beams are exposed to combined actions of impact and blast loads. Furthermore, the tU of 10 and 30 mm are recognized as the optimal UHPFRC thicknesses in strengthening the SD beam under the sole impact and combined impact-blast loads, respectively. However, tU=20 mm is accepted as an optimal thickness for the AR beams under sole and combined loads. Also, the applications of UHPFRC on the two sides of the SD beam and as a U-shaped cover represent more efficient designs in the strengthening trend of the SD beams when subjected to the sole impact and combined impact-blast loads, respectively.
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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 | 0.000 |
| 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".