Evaluating Tire Pressure Control System to Improve Productivity and Mitigate Pavement Damage
Bibliographic record
Abstract
The introduction of the use of Tire Pressure Control Systems (TPCS) to improve the productivity of the Canadian trucking industry is gaining momentum. The imposition of seasonal load restrictions (SLR) on the thaw-weakened secondary roads interrupts the transportation of raw materials to processing facilities For the forestry industry in particular, this has very significant impacts on productivity and costs. FPInnovations-Feric Division (Feric) has investigated the potential for TPCS-equipped trucks to travel with full, legal loading during the SLR period without accelerating road wear and tear. The TPCS monitors and adjusts the inflation pressure of the trucks’ tires while driving and allows the operator to optimize the inflations for changes in loading, travel speed, or road quality encountered in the trip. \n \nThis thesis describes an investigation to determine whether TPCS can be used to mitigate traffic generated damage to secondary roads and also reduce the need to implement load restrictions. The project involves a partnership with the Ontario Ministry of Transportation Ontario (MTO), Forest Engineering Research Institute of Canada (FERIC), Ontario Ministry of Natural Resources (MNR) and the Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo. \n \nThe thesis will describe the methodology, design, and instrumentation of the two test sites which are located in Dryden, Ontario and Chapleau, Ontario. In addition, repeated Portable Falling Weight Deflectometer (PFWD) testing is being carried out at these sites and the initial results of this examination and associated impacts of the environment and traffic on the road will be presented. This study also involves looking into the reliability of using the portable FWD, offering a lower cost alternative instead of the trailer mounted FWD to monitor pavement strength for the identification the SLR period. The use of innovative sensors and data collection techniques are proving to be very informative and are advancing pavement engineering knowledge. Moreover, the thesis is aimed at exploring the possibilities of achieving the current objectives of the government DOTs such as TPCS potential for addressing the timber industry in crisis, reduced road maintenance budgets, and global warming increasing road damage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".