Damage evolution models of static pre-loaded concrete under impact load based on the Weibull distribution
Bibliographic record
Abstract
Affected by static loads, ahead of the dynamic loads such as blasting, flowage, and earthquakes, concrete structures are already in various degrees of damage. Against this engineering backdrop, the damage degree should be considered in seismic design and disaster mitigation for concrete structures. To investigate the dynamic mechanical properties, energy evolution, and fracture mechanism of damaged concrete under dynamic load. The damage models of the dynamic compressive strength, energy dissipation density, and fragmentation characteristics related to the damage degree based on the Weibull distribution are theoretically studied and demonstrated by experiment. Uniaxial loading with diverse pre-loads is employed to make the concrete in varying damage degrees. The Split Hopkinson Pressure Bar (SHPB) is employed to investigate the damaged concrete's dynamic mechanical properties, energy evolution, and fracture characteristics. The experiment demonstrates the rationality of the theoretical study, the growth ratio of energy dissipation density, reduction rates of dynamic strength, and the average diameter of the fractured concrete all follow the Weibull distribution. The energy dissipation density and fragmentation degree of the damaged concrete under impact load increase with pre-load rise, while the dynamic strength and transmission energy are opposites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".