Pavement Rehabilitation Selection Based on Mechanistic Analysis and Field Diagnosis of Falling Weight Deflectometer Data: Virginia Experience
Bibliographic record
Abstract
The effectiveness of using the field diagnosis and falling weight deflectometer (FWD) mechanistic analysis in reducing a 65-km (40-mi) segment of asphalt pavement to project level segments is discussed, along with selecting a cost-effective rehabilitation strategy. A mechanistic-based analysis was performed on the deflection basins measured from I-85 in Virginia to backcalculate the layer moduli. The 65-km segment was divided into structurally homogeneous sections based on the back-calculated layer moduli. The data of each homogeneous section were analyzed further to assess the in situ structural capacity, to identify weak layers, to estimate the remaining structural life, and to determine the current and future rehabilitation needs. It was found that some sections have almost no remaining structural life, and others have remaining structural life of more than 10 years. A comparison was made between the FWD–field diagnosis rehabilitation program and a visual inspection rehabilitation program. Results of the comparison indicated that the visual inspection rehabilitation program resulted in selecting thicker overlays for some of the project sections (overdesigned) and thinner overlays for the other sections (underdesigned). It is estimated that the difference between the FWD–field diagnosis rehabilitation program and the visual inspection rehabilitation program for the overdesigned sections is in the range of 45 percent of the construction cost (savings). Life-cycle cost analysis (LCCA) was performed to quantify the difference between the two rehabilitation programs for the underdesigned sections. Results of the LCCA indicated that the FWD–field diagnosis rehabilitation program would result in 26 percent and 42 percent reduction in the construction cost and user delay cost, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".