Smart structures using shape memory alloys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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: R<sub>r</sub>, Q<sub>r</sub>, R<sub>e</sub>, and Q<sub>e</sub>, 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 it