A Parametric Study of Jointed Plain Concrete Pavement Using Finite Element Modeling
Bibliographic record
Abstract
Concrete pavements face various types of distresses such as longitudinal, transverse, and joint cracking due to traffic loading and thermal stresses. The objective of this investigation was to develop Three-Dimensional Finite-Element Models (3D-FEM) to assess the performance of dowel in Jointed Plain Concrete Pavement (JPCP).Finite-element modeling is a powerful tool that can be used for the simulation of the structural response of pavements under the effects of different loading condition. Most of the previous studies ignored important factors, including the combined effect of dynamic axle loads and thermal gradient. Overcoming the shortcomings of the previous studies, this study investigated the pavement response under the effect of some model parameters. The result of the study was verified by a comparison with field measurements. Results also showed that the combined negative gradient and axle loads located at the transverse joint subject the top of mid-slab, to high tensile stress that may explain the initiation of top-down cracks. These stresses increase under corner loading when the slab length is increased. In general, the study presented that the developed 3D-FEM is suitable for identifying the effect of different design features including pavement geometry, material properties, thermal gradients, and axle load and configuration on the structural response of rigid pavements.
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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 | 0.001 | 0.000 |
| 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".