Diagonal failure analysis of unreinforced solid clay brick masonry walls: comparative analytical and statistical strength evaluations
Bibliographic record
Abstract
Unreinforced masonry (URM) components constructed with solid clay bricks are intrinsically vulnerable to failure under seismic loading, despite being prevalent in many earthquake-prone countries. When box behaviour is ensured through e.g. adequate wall-to-diaphragm connections, in-plane (IP) shear failure often governs the building-scale seismic performance, which can be sudden and brittle. Accurate estimation of diagonal tension strength is therefore critical for evaluating failure mechanisms of URM buildings, assessing their seismic capacity, and informing effective retrofit strategies. To this end, this study presents the first comprehensive and critical review of experimental investigations on the diagonal tension strength of URM walls made of solid clay bricks, based on which a novel database of 116 experimental tests is compiled. Statistical evaluations across diverse configurations reveal that certain experimental factors, such as specimen origin, number of leaves, and wall size, exhibit clear trends in their influence on diagonal tension strength. Existing predictive equations are also assessed against the compiled data, where substantial conservative bias and significant prediction scatter are observed. To explore complementary approaches, seven machine learning algorithms are developed and compared, with hyperparameters optimized through Bayesian optimization. All data and models are made openly accessible to support future research and practical implementation. The outcomes of this study offer a robust benchmark dataset, a critical assessment of influencing factors and existing predictive models, and a demonstration of the potential of machine learning as a complementary tool for improving predictive capabilities in the seismic assessment of 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".