Dimensionamento de pilares de concreto armado de seções retangular e circular maciça submetidos à flexão oblíqua composta utilizando redes neurais artificiais
Bibliographic record
Abstract
In recent years, important advances in the field of the development of artificial intelligence tools have been obtained in practically all the areas of the scientific knowledge.The systems inspired by biological neural networks have been seen as a promising tool that is being successfully used in the solution of several problems in almost all areas of the technical-scientific knowledge.The paper explores the use of Artificial Neural Networks for design reinforced concrete sections subjected to combined axial load and biaxial bending moments.In a general way, this problem does not have an analytical solution and the computation of reinforcement is often an iterative process.In this context, the paper used Artificial Neural Networks techniques to assess the mapping between variables in reinforced concrete columns design.Feed forwad networks with back propagation training algorithm were used with more than 400 data for each type of cross section studied.Obtained results indicated good performance in real design conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".