Deflection-Based Metric for Identifying Critical Pavement Sections against Subgrade Shear Failure under Superheavy Loads: A Case Study of Alberta, Canada
Bibliographic record
Abstract
Movement of superheavy loads (SHLs) plays an essential role in various industries by facilitating transportation of heavy payloads. These industries including oil, chemical, electrical, and defense rely on specialized transport solutions to move large and heavy equipment and components, which contribute significantly to the nation’s economic prosperity. SHL vehicles have unique tire and axle configurations designed to handle large weights that ensure safe transportation. As a result, special permits are typically required for SHL vehicle moves on U.S. highways since the structural design of a standard highway does not consider the extreme weights imposed by SHL vehicles. Geographical variations along a SHL movement route (e.g., varying pavement layer configurations, subgrade soil types and strengths, as well as the presence of side slopes and buried utilities) can pose significant challenges to state highway agencies (SHAs) for permit issuance decisions. During the permitting process, agencies responsible for issuing permits commonly have short timeframes with limited pavement structural data and resources to perform the safety assessments along the entire SHL movement route. Consequently, SHAs can benefit from a competent approach to properly identify critical pavement sections that are most likely to be at risk under the imposed SHL vehicle move. In this study, an analysis framework was established leveraging falling weight deflectometer center deflection data (D0) to effectively identify critical pavement sections, thus reducing the required analysis time, making it more adaptable by SHAs. The analysis framework to identify critical sections was validated by performing an SHL move case study in the province of Alberta. Diverse data sources were utilized to substantiate the proposed analysis framework, encompassing data from the pavement management system, field pavement condition, nondestructive testing, and mechanistic analysis by employing the Superheavy load Pavement Analysis PACKage (SuperPACK).
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".