Accelerated Laboratory Evaluation of Joint Sealants Under Cyclic Loads
Bibliographic record
Abstract
There is a need to establish performance-based laboratory procedures for testing of joint and crack sealants.This research proposes a cyclic loading laboratory test procedure to be performed at in-service temperatures, which applies to hot-pour sealants.Two types of hot-pour sealants were evaluated in this research to predict resistance to tensile and compressive extension.The test results at three temperatures *30oC, OoC and -30'C presented in this research highlight distinct differences in the behavior of low and standard modulus sealant types and confirm the superior performance of the low modulus sealants.Low modulus sealants are typically able to withstand larger extension.The accelerated testing compared sealants subjected to displacements similar to traffic and temperature loadings in the field.In general, and based on the limited number of sealant products tested, Type I sealants performed poorly when compared to Type IV sealants.Both Type I sealants and two Type fV sealants failed prematurely at the 0'C and -30oC temperatures.The optimized selection of joint sealant products can extend pavement service life and reduce annual maintenance and rehabilitation needs particularly in regions which experience extreme climatic conditions.Tluee criteria were used to rank the sealants: percent load drop versus temperature, normal stress analysis and maximum surface stress analysis.Each method allowed the sealants to be grouped into three categories, sealants that performed well, sealants with average performance and sealants with poor performance.From the three criteúa, rankings were applied to the sealants as follows: Sealants D and E had good performance, sealants F, G and H performed satisfactory and sealants A, B and C performed poorly.More emphasis was placed on the low temperature results from each criterion which gives better performance rankings to sealants D and E as opposed to sealants F, G and H.The use of the field data would greatly enhance this research and would allow for models to be built allowing the prediction of the performance of hot-pour joint and crack sealants in the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".