Influence of moisture infiltration on flexible pavement cracking and optimum timing for surface seals
Bibliographic record
Abstract
Moisture increase in pavement subsurface layers has a significant influence on granular material properties that affect the expected pavement performance. In situ moisture variations over time in an unbound base layer depend on water infiltration after precipitation and pavement surface conditions. Consequently, base resilient modulus (MR) is reduced, which leads to premature failure and reduced service life. This paper presents long-term pavement performance (LTPP) data analyses for quantifying the effect of moisture infiltration through surface cracking on flexible pavement performance. Subsurface moisture data obtained through the seasonal monitoring program (SMP) time domain reflectometry (TDR) are an excellent source for quantifying the moisture-related damage in flexible pavement located in different climates. An artificial neural network (ANN) model was developed based on the SMP data for flexible pavement sections. The results show that higher levels of cracking will lead to an increase in moisture levels within the base layer, which leads to a significant decrease in the base MR. For flexible pavement, the maximum reduction in base MR ranged from 18% to 41% and from 153% to 175% for the pavement sections located in dry and wet regions, respectively. Consequently, the performance of pavement sections located in wet climates is adversely affected. The findings imply that an adequate and timely preservation treatment for cracking sealing (e.g., surface seals) can enhance the pavement’s service life, especially in wet climates. The results suggest that cracks should be sealed when the extent of fatigue cracking is within 6% and 11% for the flexible pavement sections located in wet and dry climates, 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".