Using DIC Techniques to Measure Ice Road Deflections under Moving Loads
Bibliographic record
Abstract
Ice roads in Northern Canada provide vital access for the transportation of supplies to remote communities and mining operations which are otherwise inaccessible by road. When vehicles drive across an ice road, waves in the ice are created which reach a maximum at the critical speed of the ice. The transportation efficiency is limited by the critical speed of the ice, therefore it is important to accurately determine critical speed without causing failure of the ice. To analyze the effect of varying truck speeds on ice deflection, a field study was conducted near Yellowknife, Canada. The majority of field tests use survey tools or instrumentation to measure the deflection which are effective however, they are also expensive and subject to operational difficulty in the cold environment. In this study, digital image correlation (DIC) techniques were used to capture and measure ice deflection due to a moving truck. The deflection results of the DIC method and the traditional survey method are compared for the moving load test. The greater number of DIC measurement points installed along the ice allowed for a wide area to be covered at one time, compared to the traditional survey methods. In addition, the cameras used for DIC did not experience the same disruptive operational errors as the survey total stations due to the cold temperatures. Overall, it is shown that this methodology is effective, reliable, and cost efficient in capturing ice road deflection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".