Seasonal and Temperature Adjustment Models of Pavement Properties from Seismic Nondestructive Evaluation
Bibliographic record
Abstract
New Jersey Department of Transportation (NJDOT) initiated a study with an objective to calibrate the AASHTO seasonal and temperature adjustment models, or to develop new ones that will take into consideration New Jersey specific environmental conditions. To achieve the objective, twenty-four pavement sections were instrumented to monitor pavement temperature and moisture, frost/heave penetration depth, rainfall and air temperature. A nondestructive testing (NDT) program was conducted on these sections for a period of two years. Seismic Pavement Analyzer (SPA) and Falling Weigh Deflectometer (FWD) were used to evaluate the pavement structural response and pavement properties (elastic moduli) on a monthly basis. From the collected environmental and NDT data, temperature and seasonal models were developed through statistical analyses, such as analysis of variance (ANOVA) and regression analysis. The scope of the project is presented and pavement evaluations using SPA are discussed. Correlation of pavement layer moduli to environmental variables from three seismic tests: Ultrasonic Surface Wave (USW), Impulse Response (IR), and Spectral Analysis of Surface Waves (SASW) are presented and observed trends are discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".