Environmental effects on the application of spring load restrictions on low volume roads in Northern Ontario / by Jeffrey Chapin.
Bibliographic record
Abstract
Many jurisdictions throughout Canada and the United States utilize Spring Load Restriction (SLRs) on low volume roads to minimize the damage during spring thaw. The main objective of this research is to develop a SLR method for use in northern Ontario (the Lakehead University (LU) method). In order to achieve this objective a detailed review and assessment was conducted on three potential SLR methods for their use on low volume roads in northern Ontario. The three methods assessed were an empirically based approach developed by the Minnesota Department of Transportation (M n/DOT), a semi-empirical approach developed by the University o f Waterloo and a thermal numerically based method using the finite element code TEMP/W. \nEach of the methods was calibrated for two study sites, Highway 569 in northeastern Ontario and Highway 527 in northwestern Ontario. These methods were calibrated using historical data collected from these study sites including air temperature data and observed frost and thaw depths determined from thermistor measurements. The Highway 569 study site \nwas calibrated fo r the 2005/2006, 2007/2008 and 2008/2009 seasons while the Highway 527 study site was calibrated for the 2008/2009 season only. The calibrated methods were then used to predict the application and removal dates for SLRs for the two sites for the 2009/2010 season. \nPavement stiffness testing was conducted during the freezing and thawing seasons at the two sites using a Light W eight Deflectometer (LWD). The purpose of LWD testing was to examine the changes in pavement stiffness resulting from progressive freezing and thawing of the pavement structure. The results of the LWD testing indicate a significant decrease in pavement stiffness during pavement structure thawing between depths of 0.2 and 0.4 m. Based on these results and an extensive literature review, a 0.3 m threshold thawing depth was \nselected to trigger SLR application. LWD testing also indicated a slight increase in pavement stiffness with in 2 weeks of complete pavement structure thawing. Using these results it was decided that, for this research, SLRs could be removed 7 days after complete pavement \nstructure thawing. \nDuring the calibration of the three SLR methods it was discovered that the Waterloo method requires significant adjustments to the frost and thaw depth algorithm coefficients at the onset of the thawing period. It was also determined that the accuracy of the thermal numerical modelling simulation is strongly associated with the boundary conditions used for the model. \nThe assessment of the three methods indicates that the Mn/DOT method can closely predict the SLR application date (within 1 to 2 days) and was less accurate in predicting the SLR removal date (within 6 to 9 days). The Waterloo and TEMP/W methods did not display the same degree of accuracy as the M n/DOT method when used in a predictive mode. \nBased on the LWD test results and the SLR calibration and prediction results, it was decided that the LU method should follow the M n/DOT method and use threshold Cumulative Thawing Index (CTI) values representative o f northern Ontario conditions as a trigger for application of the SLRs. In this method air temperatures are adjusted by reference \ntemperatures which are then used to calculate a CTI. When the CTI exceeds a value corresponding to a 0.3 m pavement structure thawing depth, SLRs will be implemented. SLR removal will be based on average pavement structure thawing duration. Furthermore, LWD testing during the predicted thawing season will be used to further develop the method by qualifying pavement stiffness reductions during the onset of thaw and stiffness rebound after complete pavement structure thawing. Also, the TEM P/W thermal numerical model will be used as a tool to further refine the LU method through assessment of other pavement structures and environmental conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".