Diagonal failure analysis of unreinforced solid clay brick masonry walls: comparative analytical and statistical strength evaluations
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it