Functional distress modelling in Portland cement concrete pavements
Bibliographic record
Abstract
Concrete is widely used as a construction material in pavements by public and private agencies that administer highway networks because of its high durability and capacity to resist large traffic loads and very rigorous climates. Nevertheless, these agencies have to estimate the evolution of pavement performance to plan and optimize the application of adequate maintenance activities, allowing the pavement to be maintained at an optimum service level throughout its lifetime. Predictive distress models of the incremental type, that is, models capable of predicting annual increments of different distress indicators in the pavement, could be very useful tools in the implementation of maintenance plans, with minimal need for previous data, especially with regard to information on cumulative traffic loads. This paper offers incremental models for distress prediction in jointed plain concrete pavements, related to joint problems such as faulting and spalling, which clearly affect the pavement ride quality. The equations obtained allow for not only the calculation of distress predictions in analyzing road maintenance policies, but also the adjustment of the original designs of these pavements, to minimize the occurrence and magnitude of distress problems.Key words: concrete pavements, distress models, pavement performance, pavement management systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".