Modelling in situ shear strength testing of asphalt concrete pavements using the finite element method
Bibliographic record
Abstract
Rutting is one of the well-recognized road surface distresses in asphalt concrete pavements that can affect the pavement service life and traffic safety. Previous studies have shown that the shear strength of asphalt concrete pavements is a fundamental property in resisting rutting. Laboratory investigation has shown that improving the shear strength of the asphalt concrete mix can reduce surface rutting by more than 30%, and the SUPERPAVE mix design method has acknowledged the importance of the shear resistance of asphalt mixes as a fundamental property in resisting deformation of the pavement. An in situ shear strength testing facility was developed at Carleton University, and a more advanced version of this facility is currently under development in cooperation with the Transportation Research Board and the Ontario Ministry of Transportation. In using this facility, a circular area of the pavement surface is forced to rotate about a normal axis by applying a torque on a circular plate bonded to the surface. The pavement shear strength is then related to the maximum torque. This problem has been solved mathematically in the literature for a linear, homogeneous, and isotropic material. However, the models for other material properties are mathematically complicated and are not applicable to all cases of material properties. Therefore, developing a model that can accurately analyze the behaviour of asphalt concrete pavements during the in situ shear test has proven pivotal. This paper presents the development of a three-dimensional finite element model that can simulate the forces applied while measuring the shear strength of the asphalt concrete pavement. A comparison between the model results and those obtained from available analytical models and field measurements proved the accuracy of the developed model.Key words: shear strength, in situ testing, finite element, asphalt, pavement, modelling.
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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 | 0.001 |
| 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) | 0.000 | 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".