Threshold values for reflective cracking based on continuous deflection measurements
Bibliographic record
Abstract
The fundamental mechanisms leading to the development of reflective cracking are differential movements from the supporting pavement structure. In this study, the deflection profiles collected using the rolling dynamic deflectometer (RDD) were used to determine the threshold values for reflective cracking. This provides a quantitative method to determine the severity of the cracks (or joints), which controls the potential for reflective cracking. Three different deflection parameters were considered: (i) sensor 1 deflection (W1), (ii) differential deflection between sensor 1 and sensor 3 (W1-W3), and (iii) multiple of baseline deflection value. The reliability concept was also incorporated such that pavement engineers can select criteria (based on predefined confidence levels) to identify locations where reflective cracking is likely to take place. Threshold values were determined from a 4 year study conducted along US Interstate Highway 20 (IH-20), and case studies from overlay projects along State Highway 73 (SH-73) and US Highway 59 (US-59) were investigated to verify the proposed threshold values. Based on the findings in this study, the RDD can identify problematic areas but can also be used to optimize the rehabilitation strategy. As evident from the SH-73 and US-59 projects, the W1-W3 deflection and 2.5× baseline deflection are better criteria than W1 deflection alone.Key words: rolling dynamic deflectometer (RDD), reflective cracking, jointed concrete pavements, continuous deflection.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".