Novel Dissipative Technologies for Earthquake Resistance of Structures with Tensegrity Structure and Super Elastic Characteristics
Bibliographic record
Abstract
This work describes the design and testing of a tensegrity seismic bracing system that acts as a lightweight mechanical amplifier for longitudinal displacements, effectively reducing inter-story drifts while dissipating energy.The proposed brace is built on a tensegrity D-bar construction with a rhomboidal shape and Shape-Memory Alloy (SMA) tendons.SMA tendons can undergo martensitic (solid-to-solid) transformations, allowing them to amplify signals into wide super elastic hysteresis when subjected to mechanical cycles with strains of up to 8% and no residual deformations.Our device's fundamental concept is the employment of SMA wire sections as the dissipating component, and preliminary results are reported in [4][5].Experimental results and seismic analysis of a benchmark structure show that the suggested SMA-D-bar braces have improved energy dissipation and re-centring capacity.The proposed bracing's excellent performance in mitigating seismic damage to the serviced building lays the path for the development of novel seismic energy dissipation devices that integrate tensegrity and super-elasticity ideas.In addition, the focus of this study is the development of a seismic bracing system with tensegrity architecture that could be helpful in seismic analysis of a structure.As mentioned previously, the setup was intended to act as a passive energy dissipation device.PED and SMA tendons were used as restitution elements.The dissipation of energy in buildings can be separated into two major categories: elastic dissipation and inelastic dissipation.In this research work, a system has been developed that relies on elastic dissipation mechanisms such as PED devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".