Factorial Numerical Analysis of Flexible Pavement Foundations with Emphasis on Groundwater Table Effect
Bibliographic record
Abstract
The presence of groundwater table (GWT) within a flexible pavement can have a pronounced detrimental effect on the mechanical response of its foundations and consequently on the structural performance of the pavement system as a whole. The related literature reveals that little work was done to rigorously model the impact of shallow GWT on the pavement's structural performance and investigate the sensitivity of this impact to the foundation's stiffness. In this paper, statistical factorial analyses were applied to numerical modeling to investigate the effects of GWT, foundations stiffness, and GWT-foundation stiffness interaction factors on the rutting of flexible pavements. A finite element model simulating the pavement foundations as nonlinear porous media governed by the Biot coupled behavior was set up first. The response of the model was evaluated for combination of design values of the GWT and foundation stiffness parameters each of which was defined at lower and upper levels (two levels-factorial design of experiments). Analysis of variance (ANOVA) method was then used to examine the effects of the factors analyzed on the pavement rutting. The analysis results showed that the GWT and its interaction effect with subgrade stiffness have significant influences on the pavement rutting. The detrimental effect of the GWT becomes more pronounced when the subgrade stiffness decreases, while such effect changes insignificantly with changing the stiffness of the granular base. The paper opens a new window for assessing the structural performance of flexible pavements under various GWT and foundation material conditions using the coupled finite element method in conjunction with the statistical factorial analysis approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".