Artificial Neural Network to Predict the Shear Strength of Partially Grouted Masonry Walls
Bibliographic record
Abstract
Determining the shear strength of partially grouted (PG) masonry walls subjected to lateral loads is complex, due to the anisotropy of masonry and nonlinear interactions between the mortar, grouted cells, ungrouted cells, and reinforcing steel. Although current masonry design codes provide equations to predict the shear strength of PG walls, most are empirical and rely on data that has not been subject to rigorous meta-analyses. Artificial neural networks (ANN) have demonstrated potential in engineering research applications to predict nonlinear relationships. This paper presents an ANN model for the shear strength of PG masonry walls using a compiled database of PG wall specimens. The effect of previously unaccounted parameters in code-based approaches is discussed. The process of synthesization and meta-analysis used to prepare the database for the ANN model is discussed. A sensitivity analysis is performed to evaluate the ANN model and gain insight for future research based on its predictions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".