EFFECT OF HIGH TIRE PRESSURES ON CONCRETE PAVEMENT PERFORMANCE
Bibliographic record
Abstract
This study, conducted by Construction Technology Laboratories, Inc. for the Portland Cement Association, investigates the impact of high tire pressures on concrete pavement performance. With the increasing use of trucks equipped with radial tires and high tire pressures, significant pavement damage has been observed across major highways in the United States and Canada. Unlike asphalt pavements, for which extensive research programs have been initiated, the effects of high tire pressures on concrete pavements have been less thoroughly studied. This research aims to fill that gap by examining how high tire pressures influence concrete pavement response, service life, and performance characteristics. Field testing across six sites in Wisconsin and Pennsylvania involved loading pavement sections with trucks equipped with tires at pressures ranging from 80 to 120 psi. Measurements of pavement strains, deflections, and the subsequent analysis suggested that increased tire pressure does not significantly affect concrete pavement response. Additionally, surveys of truck tire pressures in various states revealed that operational pressures are substantially higher than those used during the AASHO Road Test, upon which many pavement design equations are based. Despite the higher pressures, the research found no significant impact on pavement performance, indicating that current pavement thickness design procedures, which do not consider the effects of increased tire pressure, may still be adequate. This conclusion is supported by both theoretical analyses and field test data, which collectively indicate that increased tire pressures up to 120 psi do not significantly affect the performance or service life of concrete pavements. The study's findings suggest that existing design methodologies for concrete pavements remain valid, even in the face of changing tire technologies and usage patterns. (Abstract generated by AI tool ChatGPT 4)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".