Development of economic and reliable tool for condition survey and middle management of asphalt pavement
Bibliographic record
Abstract
Rehabilitation and maintenance of pavement are well established and costly operations.One of the major challenges of these operations is determining the optimum maintenance and repair schedule.In any given city, there are hundreds of miles of pavement constructed at different times and are in different states of deterioration.It is the job of municipal engineers to establish a cost effective schedule that prioritizes the repair or even the reconstruction of different segments of the roads.The schedule is usually based on an overall Pavement Condition Index (PCI).Typically, inspectors are sent out to observe the pavement conditions and conduct accurate measurements to be used in computing the PCI; however the cost associated with such inspection missions is often high.This thesis proposes a novel approach to estimate a cost effective Pavement Indicator (PI) for the entire city (or any area of interest).The proposed approach exploits newly available miniature cameras, GPS, wireless networking and Digital Signal Processing to automatically and continually collect visual information about different segments of the road, and combines these images to establish a live map of the city roads where different colours correspond to an approximate estimate of a pavement indicator (PI).The proposed technique is not a replacement of the traditional inspection, but rather it is a tool to identify the sections that are in greater need of repair.The technique involves taking pictures of various sections of the road network using cameras mounted on public vehicles and transmitting these images to a processing centre.Each image is processed using image filtering techniques to produce an initial estimate of PI.The cumulative effect of these estimates produces regional estimates that become more List of
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".