Dynamic response of reinforced concrete sheds against the impact of rock block with different shapes and angles
Bibliographic record
Abstract
This study aimed to quantitatively identify the influence of the block impact angle and block shape on the impact effect of reinforced concrete (RC) sheds. The smooth particle hydrodynamic (SPH) method and finite element method (FEM) were coupled and used to solve the simulation difficulty of large deformation of the sand buffer layer in the RC shed. SPH was used to simulate the sand buffer layer in the impact area that experienced large deformation. Concrete, reinforced, bedrock, block and sand buffer layer in the non-impact area were simulated by FEM. The accuracy of the coupled model was verified by the full-scale test data. Finally, the impact forces and the dynamic responses of the RC shed were analyzed, focusing on the effects of the block impact angle and shape. The numerical results show that the coupled SPH-FEM method is effective for simulating how block impacts the RC shed. The block impact angle and shape can significantly influence the normal and tangential impact forces. The design of the RC shed based on the assumption of blocks as free-falling spherical projectiles can lead to inaccuracy in some impact scenarios, and an appropriate correction was put forward in available empirical calculating formulae to include the effect of the irregular shape and impact angle of the block. The sand buffer layer dissipates a large part of the energy, which accounts for at least 58% of the initial impact energy.
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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 | 0.001 |
| Research integrity | 0.000 | 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".