Mechanical Property Test and Damage Evaluation Analysis of Steel Fiber Stainless-Steel Reinforced Concrete Beams (SFSRCBs) under Impact Load
Bibliographic record
Abstract
Abstract In this paper, the ultra-high drop hammer impact test system is adopted for the vertical impact experiment on six SFSRCBs with steel fiber volume fraction of 2.0% and build the relevant numerical model. The research variables mainly include impact energy, impact mass and cumulative impact mode. The results show that, under a single equal energy impact, with the increase of the impact speed, the crack distribution of the specimen gradually tends to the mid-span local position. Compared with the impact mass, the impact speed has a greater effect on the failure mode, deformation resistance and deformation recovery ability of SFSRCBs. When the total cumulative impact energy is the same as the single impact energy, then the overall damage of the specimen caused by high-mass low-velocity cumulative impact is less significant than that caused by low-mass high-velocity cumulative impact. Combined with the finite element analysis, the calculation formula of the maximum deflection of SFSRCB under impact load is obtained. Finally, the calculation method of the impact damage evaluation factor of SFSRCBs is proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".