Artificial neural network model for deflection analysis of superelastic shape memory alloy reinforced concrete beams
Bibliographic record
Abstract
The need for a new model capable of accurately predicting the deflection of shape memory alloy (SMA) reinforced concrete (RC) beams is clear from the results obtained in the companion paper. In the present paper, artificial neural networks (ANNs) are utilized to develop such a model. The objective is to create a design tool for computing a reduction factor β to be used in the calculation of the effective moment of inertia for SMA RC beams. First, a database was developed using the results obtained from the parametric study reported in the companion paper. The main factors affecting the moment of inertia have been considered. The network architecture that results in the optimum performance was selected and trained. After demonstrating the network’s ability to predict output data for unfamiliar input data, the network was used to develop a design chart that provides the reduction factor β as a function of the reinforcement ratio and the reinforcement modulus of elasticity. A design example is discussed to illustrate the advantages of using the developed design chart over existing models.
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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 | 0.001 |
| 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 | 0.001 |
| 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".