Uncertainty in the numerical modelling of masonry triplet tests under dynamic loading
Bibliographic record
Abstract
Masonry structures have been broadly used worldwide for centuries.These structures are likely to be subjected to seismic movements, malicious or accidental blast and/or impact loading.Expanding the current body of knowledge of how masonry structures perform under such conditions and developing reliable and robust modelling techniques is essential to improve both the efficiency and safety of the design and retrofitting of such structures.A great deal of research is currently ongoing to understand the behaviour of masonry under shock and impact loading, and this is proving to be a challenging endeavour.Masonry construction on the whole is surrounded by a high degree of variability ranging from the heterogeneity of the materials used, the degree of workmanship during construction and the uncertainty regarding the physical and mechanical properties of the brick-to-mortar interface.Masonry is known to experience a dynamic enhancement of its strength properties when subjected to impact loading and dynamic increase factors (DIFs) have been used to adjust static masonry properties accordingly when subjected to this type of loading.These DIFs are derived from sparse experimental tests and their use can be severely limited to the conditions of the tests performed, and the results obtained can carry a high degree of uncertainty.This paper considers the uncertainty present at the brick-to-mortar interface, by using Monte Carlo simulations, when subjected to dynamic loading in a standard triplet test using LS-DYNA.The results of the modelling have been compared, contrasted and discussed for use in a larger research project on the robust characterisation of masonry structures when subjected to blast and seismic loading.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".