Mechanistic Design: A Modeling Case Study for the City of Saskatoon
Bibliographic record
Abstract
This paper describes a road design case study for City of Saskatoon new subdivisions. Laboratory testing was conducted to assess the soil and aggregate material properties of sampled subgrade. Both conventional and mechanistic materials libraries were developed for subgrade and other pavement structure materials. The conventional materials library included material properties such as gradation, plasticity, and density and the mechanistic library included material properties such as dynamic modulus and Poisson's ratio. The model used these material properties as inputs and the finite element method to perform simulations and generate model outputs. For this project, the outputs were different pavement structure design options based on maximum peak surface deflections allowed. For this new subdivision design case study, standard conventional pavement structures currently used for local roads and collector roads were modeled as a baseline and compared to alternative road structures with additional base material, a sand drainage layer, and a rock drainage layer for one subgrade type. For each resultant cross section generated by the model, the shear strains were analyzed and maximum peak surface deflections were used to assess which cross section was optimum for each subgrade type. The results of this case study showed that the mechanistic model provided feasible alternative pavement structures for City of Saskatoon local and collector roadway design. This study illustrated that using the standard pavement structures for local roads currently used by the City's design methodology may not be structurally appropriate for roads in new subdivisions with varying subgrade types. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.000 | 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 | 0.000 | 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".