Seismic Response Control of Benchmark Building using Semi-active Shape Memory Alloy based Tension Sling Damper
Bibliographic record
Abstract
Seismic protection of building has remained important due to increased seismic activities as well as stringent seismic performance criteria.Recent past has seen surge in utilization of smart materials with controllable properties for seismic performance enhancement of structural systems.NiTinol Shape Memory Alloys (SMA) exhibiting super-elasticity and shape memory effect makes it a promising candidate for seismic protection applications.Novel SMA based tension sling damper (SMA-TSD) is developed incorporating super-elastic SMA slings and temperature controlled SMA springs.It is placed at ground story of a three story benchmark building for both; passive and semi-active seismic protection.Hysteretic behaviour of SMA-TSD is characterized by one-dimensional Tanaka model and is mapped to equivalent linear Voigt model for implementing it with linear benchmark building.Desired damper force of semi-active SMA-TSD is evaluated by Linear Quadratic Regulator (LQR) control strategy and realized by Shape Recovery Force (SRF) of SMA springs through SRF-Temperature-strain relationship.SRF is applied to expand passive hysteretic loop of super-elastic SMA slings within recoverable strain limit of 6%.Peak displacement, peak inter-story drift and peak acceleration responses show moderate reduction of the order ̴ 12%-39% for passive off control strategy except peak acceleration at first story, substantial reduction of the order ̴ 47%-71% when passive on and LQR control strategies are used for controlled benchmark building subjected to Taft seismic excitation.LQR control strategy yields substantial reduction in peak seismic parameters by realizing peak damper force in SMA-TSD as low as 284.90 N. Temperature is varied between practical range of 20 0 C to 60 0 C for generating SRF from SMA springs commanded by 5V battery only.Design parameters of SMA-TSD; diameter, number and length of SMA sling/s can be optimized to obtain enhanced seismic performance of the benchmark building.
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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 | 0.000 |
| 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".