Investigation of the relationship between the strength limit and the long time fatigue of steel reinforcements of reinforced concrete structures
Bibliographic record
Abstract
It is known that the fatigue process begins with the plastic deformation of the surface layers of the metal fittings. Moreover, the displacement of dislocations under conditions of re-alternating loads is observed at loads below the elastic limit of the metal. The rate of local plastic deformation during cyclic deformation is several orders of magnitude higher than the rate of deformation under static loading. Dislocation slip begins in grains with a favorable orientation near stress concentrators. As the number of cycles in the surface layers increases, the density of dislocations and the number of vacancies increases. When the base number of NR cycles is reached, a surface reinforced layer of metal with a large number of germinal cracks is formed, the size of which does not reach a critical value. Increasing the number of cycles cannot cause further development of fracture in such a layer. Only when the stresses exceed the endurance limit of the crack reach a critical length, after which the process of their discharge into the main crack begins with the spread of the latter. The results of experimental studies indicate a strong effect of diffusion hydrogen on static and cyclic parameters of crack resistance. It was found that with increasing flooding, especially when the hydrogen content exceeds 5 cm 3100g, both static strength and long-term strength (fatigue) decrease sharply. Moreover, for these areas of hydrogen solution in reinforcing steel is characterized by a viscous nature of fracture, while for heavily flooded reinforcement (from 5 to 12 cm3100g is characterized by brittle fracture by the mechanism of microfission in the hardened (martensite or troostite structure). allowed to determine the optimal hydrogen content in reinforcing steel (3-5 cm3 100g), the excess of which will reduce the crack resistance of reinforcement during long-term operation, especially in corrosive environments. The results of the research confirm the above data. bainite structure y sharply reduces the crack resistance of reinforcing steel, which makes it impossible to use in the manufacture of reinforcement involved in reinforced concrete structures designed for long-term operation (more than 50-60 years). Thus, the obtained diagram can be recommended to designers of reinforced concrete structures for hydraulic purposes, as it greatly facilitates the reasonable choice of reinforcement in the development of reinforced concrete structures for responsible and long-term use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".