Artificial neural networks for predicting creep with an example application to structural masonry
Bibliographic record
Abstract
Numerous creep models with limited accuracy have been developed within the last few decades to predict creep of concrete and masonry structures. The stochastic nature of creep deformation and its reliance on a large number of interdependent parameters (e.g., the brick and mortar types, the relative humidity, and the history and level of applied loading) make developing a single, general, and yet accurate mathematical model almost impossible. Artificial neural networks (ANNs) have been recently introduced as an efficient artificial intelligence modeling technique for applications incorporating a large number of variables. Artificial neural networks have proven successful in many instances where conventional mathematical modeling techniques were not as accurate or capable. Here, the potential use of ANNs in predicting creep is examined. A new ANN model is applied to the prediction of creep of structural masonry. The ANN developed is able to predict the creep performance with an excellent level of accuracy compared with that of conventional models.Key words: creep, artificial intelligence, neural networks, viscoelastic deformation, masonry structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".