Concrete-Filled, Glass Fiber–Reinforced Polymer Dowels for Load Transfer in Jointed Rigid Pavements
Bibliographic record
Abstract
Smooth, round steel dowels have been used for nearly a century to transfer wheel loads across concrete pavement joints. Dowels are subjected to shear and bending stresses caused by traffic loads in addition to curling stresses caused by temperature gradients in pavement slabs. Over time, the use of deicing salts corrodes steel dowels and causes damage to concrete pavement joints. Alternative dowel materials, such as stainless steel and glass fiber–reinforced polymer (GFRP), have been introduced in recent years. The dominant size of dowels for highway pavements has remained the same, typically 38 mm in diameter, and costs can be considerably higher for stainless steel compared with epoxy-coated steel dowels of the same size. Experimental tests at the University of Manitoba, Canada, examined the performance of four dowel types, including the standard 38-mm epoxy-coated steel; 38-mm solid, pultruded GFRP dowels; and 50-mm and 63.5-mm concrete-filled GFRP tube dowels. The dowels were cast in small concrete slabs of typical pavement thickness and instrumented with strain and displacement gauges. Behavior of the dowels was evaluated on the basis of measured displacements, bending strains, and performance for more than 1 million load cycles. Concrete-filled GFRP tube dowels exhibited considerably smaller displacements and, therefore, lower bearing stresses than 38-mm steel and solid GFRP bars. After 1 million load cycles, concrete-filled dowels and concrete slab showed no signs of fatigue damage or loss of load transfer, indicating a reasonable potential for replacing steel dowels, particularly in corrosive environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".