Bibliographic record
Abstract
Wood is one of the earliest materials that has been used for making railway sleepers.The sensitivity of wood to atmosphere and fire and consequently their early destruction lead to the application of novel materials such as concrete to cover the deficiencies of wood.Although concrete solely does not have high tensile strength and after a while, with the existence of microcracks, it would be destroyed.In addition, Thus, wherever rapid vibration damping is required, additives should be applied or the sleepers' material must be changed.In this study, by application of fuzzy algorithm and membership functions, the most suitable material for construction of sleepers based on absorbing energy is investigated.The aforementioned approach requires preparing a list of candidate materials.For instance, if the selected material is reinforced concrete with basalt fibers, the best proportion of concrete and fibers should be calculated.The applied equations are Euler equations for two fixed-end beam and American standard is used for changing load.Moreover, in order to represent the selected material and detailed examination, appropriate finite element software is applied.The final choices for the materials of sleepers are reached by the combination of the software results.In addition, the study of the displacement and stress in reinforced sleeper based on the selected material is done and due to negligible displacement and positioning of the stress in compressive direction, the weak point of concrete in tensile load capacity is covered and the capacity of vibrating load in the proposed model is enhanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.940 | 0.924 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".