Mechanistic Design Sensitivity Analysis for City of Saskatoon Pavement Structure Design
Bibliographic record
Abstract
Currently, City of Saskatoon (COS) pavement structure designs are based on Saskatchewan Ministry of Highways and Infrastructure (MHI) design protocols for roadways in addition to the City of Saskatoon New Neighbourhood Design and Development Standards Manual. The MHI design protocols use California bearing ratio (CBR) modified Shell design curves, which are based on the American Association of State Highway Officials (AASHO) road test. In new Saskatoon subdivisions, many of the in situ subgrade soaked CBR measurements are less than 5.0. In those cases, the City’s New Neighbourhood Standards Manual calls for the pavement structure design to be conducted on an individual basis, with drainage layers, weeping tile, and geosynthetics. Neither the New Neighbourhood Standards Manual nor the MHI design protocols account for the use of drainage layers, weeping tiles, and/or geosynthetics in the design processes. A new design methodology is needed. Given observed premature structural failures of relatively new pavements in Saskatoon in areas of poor soils and/or wet conditions, a mechanistic roadway design methodology for Saskatoon roadways was conducted as part of a pilot study in 2013. The design methodology that was used employed mechanistic materials testing and three dimensional road structural modeling. This pilot study showed that the standard City of Saskatoon pavement structures for roadways were not structurally appropriate for roadways in subdivisions with marginal subgrade types. Mechanistic modeling provided an alternative design methodology based on peak surface deflection critical state criteria and in situ material properties specific to actual traffic loadings and moisture conditions. This paper examines the sensitivity of peak surface deflection model response relative to granular base thickness with and without drainage layers. Using results of the mechanistic pavement structure design study, these pavement structures were evaluated into three levels of risk based on modeled peak deflections: low risk of failing, some risk of structural failure, and high risk of structural failure. Levels of risk were established for thickness of granular base layer as well as construction costs. This allows the pavement designer to determine the optimum pavement structure using desired layer thickness or budgetary requirements, all based on modeled structural primary response under field state conditions typically encountered in the Saskatoon’s new subdivision areas.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".