Measuring the Impacts of Climatic Exposure to Pavement Surface Deterioration with Low Cost Technology
Bibliographic record
Abstract
Pavements play a significant role in social and economic development. Canada spent approximately 12 billion dollars annually on pavements. However, roads are exposed to climatic changes and truck loads which affect their serviceability and reduce their lifespan. Most of Canada is exposed to freeze-thaw cycles which have a drastically impact on the pavement structure. A large portion of the deterioration occurs during the spring thaw period. \n\tThis research uses a smartphone to estimate pavement roughness on a weekly basis during 30 weeks in an attempt to test if such indicator can be used to identify the beginning of the load restriction and the overall damage experienced after one environmental cycle. A pavement section located on highway 20 near Montreal was visited during 2016 and 2017 season. The studied segment is about 8 km long. A pavement roughness index (RI) was estimated before, during, and after the winter season. The air temperature was registered in order to characterize the number of freeze thaw cycles experienced. It was impossible to use the RI measurements to identify the beginning of the thawing period as RI reflected the wheel-path driven and in many occasions changed were imperceptible. It was only after taking dates with larger time separation that overall decay in roughness condition was observed. \n\tOne day during Fall, Winter, and Spring selected as an excellent case to present the freeze-thaw cycle effect and to show the variations in the pavement surface condition. It has been found that the freeze-thaw cycle impacted the subgrade soil layer which reflected on the pavement surface. The average RI value before the frost season was found to be 3.99 m/km in average, while during winter season was 4.52 m/km, and in spring season was 5.30 m/km on average. The pavement deterioration was increased by average of 1.31 m/km. The results of RI change were then transferred into other Canadian location with dissimilar freezing index and annual precipitation, and annual impact of expected roughness decay estimated for various cities.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".