Improving the Efficiency of a Novel Controlled-Sliding-Based Isolation System for Brick Masonry Structures
Bibliographic record
Abstract
Low-rise brick masonry buildings are considered the most vulnerable type of structures when they are subjected to seismic excitation. Base isolation is considered a widely adopted strategy to enhance the seismic performance of low-rise brick masonry buildings. Previously developed sliding-based isolation systems did not specifically determine the most adequate combination of isolation layer thickness and the recentering mechanism that will result in the best isolation performance. Accordingly, this study investigates the identification of the best-performing configuration of the previously developed base isolation system through extensive numerical studies and experimental verification. The critical parameters considered in this research are the suitable thickness of the isolation layer and the spacing of the recentering rebars. A finite element analysis was conducted on a 1∶3 reduced scale unconfined brick masonry wall model. The first set of models was having a constant isolation layer thickness of 63.5 mm and four different values of recentering rebars spacings (i.e., 152.4, 203.2, 254, and 304.8 mm). The second set of numerical models consists of varying isolation layer thicknesses such as 50, 63.5, and 76 mm, and a constant recentering rebars spacing of 152.4 mm. It was concluded that the suitable value of isolation layer thickness is 63.5 mm with recentering rebars located at a distance of 152.4 mm because it gives the maximum amount of seismic energy dissipation. Later on, the isolator was experimentally verified using a reduced scale unconfined brick masonry wall subjected to displacement controlled cyclic loading tests. Finally, a case study was conducted to verify the performance of the proposed isolator in a full-scale school building.
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) | 0.000 | 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 | 0.001 | 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".