Shake-table response simulation of a URM building specimen using discrete micro-models with varying degrees of detail
Bibliographic record
Abstract
Abstract Recent technological advances have enabled earthquake engineering researchers to develop numerical models of increasing complexity, capable of duly reproducing even the smallest structural detail. In the case of unreinforced masonry (URM) structures, however, because of their discrete and heterogeneous nature, computational performance tends to decrease exponentially as a function of the adopted refinement level, thus confining the applicability of advanced micro-models, according to which each masonry unit is typically modelled separately, to reduced-scale problems. To enable their use at a building scale, and benefit from considering simultaneously out-of-plane failures, local wall-diaphragm interaction and collapses, researchers often need to decrease the level of detail of specific members or sub-structures. In the current literature, however, the influence of the abovementioned simplifications on the quality of micro-modelling predictions has been only marginally investigated so far, while code-based guidelines are missing. To start addressing such knowledge gap, the dynamic response of a shake-table-tested full-scale URM building specimen has been simulated in this work using a very detailed micro-model, and the results obtained were then compared with those of nominally identical models in which, however, the idealisation of some specific structural elements has been purposely simplified. Aimed at further extending the impact of this study, pushover analyses were also performed using the same models. Preliminary outcomes, which may serve as a reference to develop more informed, effective and targeted multi-scale micro-modelling strategies in the future, indicate that: (i) maximum base shear predictions tend to be less impacted by the introduction of modelling simplifications, (ii) despite requiring more labour, the explicit representation of the brickwork pattern generally led to better results in terms of predicted damage propagation, failure mechanisms and displacement capacity, (iii) using equivalent membranes, as opposed to modelling each component of timber diaphragms, provided acceptable results, making it a plausible alternative for practical applications of micro-modelling approaches.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".