Two applications of wavelet analysis for project level pavement management
Bibliographic record
Abstract
Wavelet analysis is a signal processing technique that can be used to decompose longitudinal road surface profile signal into a number of wavebands.The outputs of the analysis include the signals (elevation vs. distance) and energies (a measure of elevation variation, i.e. surface roughness) in the different wavebands.The application of wavelet analysis in road pavement management at project level is described herein through two case studies.The first involves using wavelet analysis outputs in identifying and locating excitation sources of dynamic wheel loads (DWL) along a road section.The second case study involves using these outputs in assessing the effectiveness of rehabilitation treatment in reducing surface roughness in the different wavebands along the length of a road section.The outcomes of this research study indicate that the proposed applications are effective in addressing the intended purposes.Study findings also indicate that using HATI to highlight sections subject to high DWL at network level is promising.However, further testing is required to confirm its suitability at different speeds and operating environments.These assessment approaches help asset managers to identify and locate surface characteristics that increase pavement damage, propose suitable treatments and assess the quality of these treatments.In addition to achieving value for money, adopting such approach would ensure their assets' sustainability, mobility and comfort of all road users, in particular truck drivers.Long wavelength roughness with high contributions to DWL also has a detrimental effect on heavy vehicle ride.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".