Development of an automated routing and pavement damage prediction program for superheavy trucks
Bibliographic record
Abstract
The implementation of North American Free Trade Agreement (NAFTA) opened the borders to international traffic flows traveling from/to both Canada and Mexico. As a consequence, the US highway network would be subject to trucks with new axle configurations and heavier axle loads. A fund study, Model Calibrations with Local APT Data and Implementation for Focused Solutions to NAFTA Problems, aims at providing tools to predict the additional pavement damage and the economic impacts of allowing such super-heavy trucks utilizing the US highway system. As part of this fund study, this research focused on developing a GIS-based tool integrating a Finite Element program to automate the selection of routes and evaluation of pavement damage caused by super-heavy trucks. This tool, referred as Pavement Damage Prediction (PDP) program, was developed using previous work conducted by researchers at UTEP for the TXDOT. The procedure uses a network representation of state highway corridors for super-heavy trucks in the New York State. It incorporated the shortest path algorithm in the platform of ArcView GIS software and Network Analyst extension. The Finite Element program was integrated to calculate the pavement distress on each road segment when a super-heavy truck was hauled along the selected shortest path. Finally, truck damage would be expressed in relative terms compared to that of a standard truck.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".