Development of an automated routing and pavement damage prediction program for superheavy trucks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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) | 0.000 | 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 it