Smart structures using shape memory alloys
Bibliographic record
Abstract
Elevated civil structure systems, such as communication towers and water tanks, are prone to higher mode vibration and earthquake induced damages. To mitigate damages, however, the structures are retrofitted with conventional (e.g. steel casing) and/or emerging techniques (e.g. smart structures). Smart structure entails integration of system behavior, control design and actuators. In this paper, utility of smart structures is illustrated through an elevated water tank concrete column. The concrete column is modeled as a continuous system, using the Lagrangian formulation, and linear quadratic regulator (LQR) is used for the control system, and shape memory alloy (SMA) for actuation. The water tank is excited with the 1940 El-Centro earthquake record. A sensitivity analysis is performed on the controller error and penalizing constants, as well as actuator location and angle of the connection. The four control variables that can be analyzed for the controller are: Rr, Qr, Re, and Qe, which are the control penalty, error penalty, measurement noise and process noise, respectively. The connection height on the beam and angle of the actuator is also analyzed for optimal performance. From the sensitivity analysis, the most efficient controller configuration is identified for further analysis of the structure. Optimal actuator configuration can be found based on the reduction of displacement versus the amount of energy used. It has been shown that using the SMA, the seismic demand on the concrete column is reduced using the SMA.
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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 | 0.001 | 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".