Analytical Solution for the Ultimate Compression Capacity of Unbonded Steel-Mesh-Reinforced Rubber Bearings
Bibliographic record
Abstract
Unbonded steel-mesh-reinforced rubber bearings (USRBs) have been proposed as an alternative isolation bearing for small-to-medium-span highway bridges. It replaces the steel plate reinforcement of common unbonded laminated rubber bearings (ULNR) with special steel wire meshes, resulting in improved lateral properties and seismic performance. However, the impact of this novel steel wire mesh reinforcement on the ultimate compression capacity of USRB has not been studied. To this end, theoretical and experimental analysis of the ultimate compression capacity of USRBs were carried out. The closed-form analytical solution of the ultimate compression capacity of USRBs was derived from a simplified USRB model employing elasticity theory. A parametric study was conducted considering the geometric and material properties. Ultimate compression tests were conducted on 19 USRB specimens to further calibrate the analytical solution, considering the influence of the number of reinforcement layers. An efficient solution for USRBs’ ultimate compression capacity was obtained via multilinear regression of the calibrated analytical results. The efficient solution can simplify the estimation of USRBs’ ultimate compression capacity while maintaining the same accuracy as the calibrated solution. Based on the efficient solution, the design process of a USRB with a specific ultimate compression capacity was illustrated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".