Analysis, Seismic Performance-based Assessment, and Design of Steel Endplate Connections and Buildings Equipped With Shape Memory Alloy (SMA) Bolts
Bibliographic record
Abstract
<p>Smart materials can be used in structures to avoid damage due to earthquakes. Shape Memory Alloys (SMA) are the most popular smart materials used in structural systems. Unlike steel material, SMAs can dissipate induced cyclic energy without noticeable strength degradation and residual deformations. Proper implementation of SMA materials in steel structures, for example, in plastic hinge regions, can improve the seismic response of structures, especially reducing the residual deformations significantly. A structure capable of returning to its plumb position following a seismic excitation is called a self-centering structure. This dissertation investigates self-centering extended endplate connections equipped with SMA bolts. To this end, 3D finite element models are developed and extensively validated by comparing the finite element results with the experimental results. The developed finite element model is then used to perform sensitivity analyses using the Design of Experiments method. Next, a backbone curve is proposed for the SMA-based connections, and the influential factors obtained from the sensitivity analyses are used in another design of experiments to develop a new database and, consequently, predictive equations for the backbone curve parameters. The generated database based on the significant factors is used to train Artificial Neural Networks (ANN) to develop more accurate predictive models while including the experimental test data. The trained ANNs are then used to develop a Graphical User Interface (GUI) for predicting the backbone curve of the connections. Additionally, the trained ANNs are used to conduct an optimization study to identify the optimal regions for the design parameters. Using the predictive tool, a phenomenological model for the SMA-based beam-to-column connections is developed in the OpenSees. The study illustrates the use of the predictive tool for accurate and efficient modeling of SMA-based connections and self-centering moment-resisting frames. The computation time for a typical SMA connection is significantly reduced from seven hours in ANSYS to only three minutes in OpenSees while providing the same level of prediction accuracy. Lastly, a performance-based seismic design is proposed. Two frames of 3- and 6-story height are designed using the proposed design framework to check the proposed design method against prescribed performance objectives.</p>
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".