Corner loading and curling stress analysis for concrete pavements an alternative approach
Bibliographic record
Abstract
Since corner breaks are one of the major structural distresses in jointed concrete pavements, this research study mainly focuses on the determination of the critical bending stresses at the corner of the slab due to the individual and combination effects of wheel loading and thermal curling. A well-known slab-on-grade finite element program (ILLI-SLAB) was used for the analysis. The structural response characteristics of a slab corner were first investigated. Based on the principles of dimensional analysis, the dominating mechanistic variables were carefully identified and verified. A series of finite element factorial runs over a wide range of pavement designs was carefully selected and conducted. The resulting ILLI-SLAB corner stresses were compared with the theoretical Westergaard solutions, and adjustment factors were introduced to account for this discrepancy. Prediction equations for stress adjustments were developed using a modern regression technique (Projection Pursuit Regression). A simplified stress analysis procedure was proposed and implemented in a user-friendly computer program (ILLISTRS) to facilitate instant stress estimations and practical trial applications.Key words: concrete (rigid) pavements, corner breaks, loading, thermal curling, corner stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".