Artificial Neural Network to Predict the Shear Strength of Partially Grouted Masonry Walls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it