Comparative evaluation of dynamic responses of RCC composite pavement under different moving axles
Bibliographic record
Abstract
Roller-compacted composite pavement is considered as an alternative for traditional pavements that provides the desired benefits of both rigid and flexible pavements in a cost-effective manner. To evaluate the performance of roller-compacted concrete (RCC) composite pavement under moving loads, a three-dimensional finite element model (FEM) was developed. A realistic dynamic moving load was applied to the surface of the pavement via DLOAD subroutines developed by FORTRAN. The dynamic response of the pavement at critical locations induced by single, tandem, and tridem axles at speeds of 8 and 80 km/h was determined using the developed FEM model and utilized to estimate pavement performance. Sensitivity analysis of design factors considering variations of hot mix asphalt (HMA) and RCC thicknesses, RCC elastic modulus, axle configurations, and moving speed was conducted to evaluate their impact on fatigue life and rutting development of RCC composite pavement. The results show that the performance of the pavement is significantly affected by axle configurations and moving speed. Among different axle configurations, the tandem axle creates the highest tensile stress and shear stress in the RCC base and HMA layer, respectively. The fatigue life of the pavement prolongs by increasing in the speed of moving loads, so it is expected that the pavement withstands higher load repetition when it is subjected to higher speed traffic. Moreover, increasing layer thicknesses and stiffness of the RCC layer improve fatigue life and rutting performance of the pavement. Even though increasing the thickness of the HMA layer enhances the fatigue life of pavement, increasing it to over 15 cm increases the potential of rutting.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".