Artificial Neural Network–Based Predictive Tool for Modeling of Self-Centering Endplate Connections with SMA Bolts
Bibliographic record
Abstract
Endplate moment connections with shape memory alloy (SMA) bolts provide self-centering for the seismic resilience of structures. Predicting the self-centering response of these new beam–column connections, which have not been codified yet, requires conducting experimental tests or detailed continuum finite-element simulations. Computationally efficient predictive tools are needed to facilitate the analysis, design, and assessment of self-centering connections and moment frames. In this paper, artificial neural networks (ANNs) are used to develop a MATLAB tool for predicting the moment-rotation backbone and self-centering response of extended endplate connections with SMA bolts. As the input for the neural networks, the predictive model development employs a design database of response parameters from 72 finite-element (FE) models and experimental tests of seven beam–column connection specimens. Neural networks are trained for seven response parameters, and the trained networks are used to develop a graphical user interface (GUI). The coefficient of determination for the trained ANNs is in the range of 0.92 to 0.99, indicating acceptable prediction accuracy. Furthermore, optimization studies using a multiobjective genetic algorithm are performed, seeking the minimization of material use (steel and SMA) and improved connection-response characteristics (i.e., stiffness, strength, and ductility). A phenomenological model of SMA connections is also developed in OpenSees. The use of the ANN-based predictive tool for accurate and efficient modeling of SMA-based connections and self-centering moment-resisting frames is illustrated. The computation time for predicting the moment-rotation response of a typical SMA connection is significantly reduced from seven hours in ANSYS to only three minutes in OpenSees while providing the same level of response prediction accuracy. Furthermore, the optimization results are confirmed by performing nonlinear pushover and response history analyses.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".