Influence of LRB Isolators on Accelerations and Shear Forces in an Eight-Story Building with Shear Walls in Comas, Lima, Peru
Bibliographic record
Abstract
Earthquakes are one of the leading causes of material losses and loss of human life worldwide.For this reason, ensuring the safety of buildings is of utmost importance.An effective solution to mitigate these risks is base isolation systems, a technology designed to reduce the impact of seismic movements on building structures.This study examines the influence of LRB (Lead Rubber Bearings) devices on the structural behavior in terms of accelerations and shear forces in an eight-story building, utilizing Etabs v22 software.The study compares structural performance through a nonlinear time-history analysis of a fixed base building against the same building equipped with a base isolation system.The results for the base isolated building demonstrate a reduction and a more uniform distribution of floor accelerations, which improves the stability of the building contents.Shear forces were significantly reduced by 72.76%, highlighting the effective flexibility of the implemented isolation system.Additionally, the energy dissipated by the LRB system achieved an efficiency of 76.5%.These findings confirm those reported in other studies and emphasize the importance of considering the implementation of these seismic protection systems in housing projects located in high seismic risk areas, such as the district of Comas, Lima, Peru.
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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 | 0.001 |
| 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) | 0.001 | 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".