Bibliographic record
Abstract
Different approaches are usually taken when designing flexible and rigid pavement: the rigid concrete slab carries major portion of the traffic load; while for flexible pavement, external loads are distributed to the subgrade because of the relatively low modulus of elasticity of asphalt layer comparing to concrete in the case of rigid pavement. Pavement engineering has gone through major developments; the transition from Empirical Design Method to Mechanistic-Empirical Methods is becoming a near-future trend. The Mechanistic-Empirical Method has two components: (1) stress, strain and deflection are calculated based on analyzing mechanical characteristics of materials; (2) critical pavement distresses are quantitatively predicted by experimental calibrated equations. Hence, stress analysis has become an important role in pavement engineering. The most practical and widely used stress analysis method for flexible pavement is Burmister's Elastic Layered Theory; and for analyzing rigid pavement is Finite Element Method. KENSLABS and STAAD-III are both Finite Element software; KENSLABS is designed specifically for concrete pavement stress analysis, therefore it is more user-frielndly for pavement design; STAAD-III is more suitable for general plane and space structures. The project compares the use of both software for stress analysis in rigid pavement in term of simplicity and precision.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".