Experimental study on the load‐carrying capacity of steel‐mesh‐reinforced rubber bearings under axial compression
Bibliographic record
Abstract
Abstract Isolation bearings play an important role in the seismic resilience of highway bridges. Flexible and high‐strength reinforcement has been applied in elastomeric isolation bearings to substitute conventional rigid steel plate reinforcement to enhance their lateral performance, for example, lower lateral stiffness and larger deformability. However, the main literature shows that existing flexible reinforcement, such as carbon/glass fiber fabric, may not guarantee a sufficient vertical load‐carrying capacity of elastomeric bearings to meet the design requirement of 30 MPa considering the vertical seismic effect. To this end, the emerging high‐strength steel woven wire mesh was introduced as an alternative flexible reinforcement for the bearings in this study to increase their ultimate compression capacity while maintaining superior lateral performance. Vertical compression tests were conducted on 34 specimens of the proposed unbonded steel‐mesh‐reinforced bearings (USRBs) to investigate the ultimate compression capacity. In addition to the general ultimate behavior of USRBs under vertical loading, the influence of various design parameters (i.e., individual rubber layer thickness, number of reinforcement layers, bearing design load) was investigated through comparisons among the specimens. From the test results, the compressive failure mechanism of USRBs was unveiled, which originated from the tensile failure of the steel mesh reinforcement. The steel mesh reinforcement was proved to increase the bearing ultimate compression capacity to an average of 52.0 MPa compared to fiber‐reinforced bearings, with 85% of specimens exceeding 30 MPa. Moreover, the compression capacity of USRBs was identified to be significantly affected by the individual rubber layer thickness. Specific discussions were further provided concerning the influence of potential manufacturing defects. Finally, suggestions were provided to further enhance the ultimate compression capacity of USRBs based on the results and discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".